Arbib
- Handbook of Brain Theory and Neural Networks |
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Book |
Page |
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Topic |
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Abbott; Activity Neuronal |
63 |
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Activity Dependent Regulation of
Neuronal Conductances |
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Abbott; Activity Neuronal |
63 |
|
The activity of a neuron in a neuronal network depends both on
the synaptic input it
receives and on its own intrinsic electrical
characteristics. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
The intrinsic
characteristics of individual
neurons can be modified
by activity. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
Roughly a dozen different types of ion channels contribute to the membrane conductance of a typical neuron. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
The electrical
characteristics of neurons depend on the number of channels of each type active within the plasma membrane and how these channels are distributed over the surface of the cell. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
A complex
array of biochemical processes controls the number and distribution of ion channels by constructing and transporting channels, modulating their properties, and inserting them into and removing them from the plasma membrane. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
Many of the biochemical
processes of neurons are affected by electrical
activity,
ranging from activity-induced gene expression to activity-dependent
modulation of assembled ion channels. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
Channel synthesis, insertion, and modulation are much slower than the usual voltage- and ligand-dependent processes that open and close channels. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
We need to understand the feedback mechanism linking a neuron's electrical
characteristics to
its activity. |
|
0 |
Abbott; Activity Neuronal |
63 |
|
Activity-dependent regulation could affect many channel properties, including the kinetics, voltage dependence, and open conductance of single channels; the number of active channels present in the membrane; and the distribution of channels over the surface of the neuron. |
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0 |
Carpenter;
Adaptive Resonance (ART) |
79 |
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Adaptive Resonance Theory (ART) |
|
16 |
Carpenter;
Adaptive Resonance (ART) |
79 |
|
Adaptive Resonance Theory (ART) was introduced as a theory of human
cognitive information processing and has led to
an evolving series of real-time neural network
models that perform unsupervised and
supervised category learning, pattern
recognition,
and prediction. |
|
0 |
Carpenter;
Adaptive Resonance (ART) |
79 |
|
A match-based learning process
is the basis of ART stability. |
|
0 |
Haykin; Adaptive Signal |
82 |
|
Adaptive Signal Processing |
|
3 |
Gentner;
Analogy Reasoning |
91 |
|
Analogy-Based Reasoning |
|
9 |
Gentner;
Analogy Reasoning |
91 |
|
Analogy is
a perception
of relational commonalities between domains
that are dissimilar on the surface. |
|
0 |
Anderson;
Associative Networks |
102 |
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Associative Networks |
|
11 |
Anderson;
Associative Networks |
102 |
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The operation of association involves the linkage of information with other information. |
|
0 |
Anderson;
Associative Networks |
102 |
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Association
is the most natural form of neural network computation. |
|
0 |
Anderson;
Associative Networks |
103 |
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Representing
neural activity patterns mathematically as state vectors, the most common neural network architectures are pattern transformers that take an input pattern and transform it into an output pattern by way of system dynamics and a set of connections with appropriate weights. |
|
1 |
Anderson;
Associative Networks |
102 |
|
Neural networks can be thought of as pattern
associators, which link an input pattern with the most appropriate output pattern. |
|
-1 |
Anderson;
Associative Networks |
103 |
|
In autoassociative networks, the input
and output patterns are identical. |
|
1 |
Anderson;
Associative Networks |
103 |
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Recurrent networks are well suited to be autoassociative. |
|
0 |
Anderson;
Associative Networks |
103 |
|
Dangerous
and striking events, causing a biochemical upheaval, give rise to what had been called "flashbulb memories" where everything, including totally irrelevant
detail, is learned. |
|
0 |
Anderson;
Associative Networks |
105 |
|
A content
addressable memory where input of partial or noisy information is used to retrieve the correct stored information. |
|
2 |
Anderson;
Associative Networks |
105 |
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The ability of autoassociative systems to reconstruct missing or noisy
parts of the learned
patterns. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
Auditory Periphery and Cochlear
Nucleus |
|
10 |
Mountain;
Auditory Periphery |
115 |
|
The auditory
periphery transforms a very
high information rate signal into a group of lower information rate signals. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The auditory
process of parallelization is essential because the potential
information rate in the acoustic stimulus is of the order
of 0.5 Mb per second, and yet typical
auditory nerve fibers
have maximum
sustained firing rates of 200 Bits per second. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The cochlear
nucleus continues the process of parallelization by creating multiple representations of the original acoustic stimulus, with each representation presumably emphasizing different acoustic
features. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The cochlea takes a serial time signal and converts it into parallel signals in the form of the mechanical
stimuli to the hair
bundles in the hair
cells located
in the organ of Corti. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
Outer hair cells appear to act collectively as a nonlinear let the mechanical
feedback system, which enhances cochlea sensitivity for low-level sounds, but
which provides little enhancement for high-level sounds. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The major
output of the second
stage of auditory processing consists of the receptor
potentials of the
inner hair cells. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The inner
hair cell receptor potential is a rectified and low pass filtered version of the mechanical
stimulus,
with an amplitude roughly proportional to the log of the stimulus power. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
The logarithmic relationship of the inner hair cell receptor is
attributable to a combination of nonlinearity in the mechanics and in the NASL transduction process, and
use the hearing apparatus is very large dynamic range. |
|
0 |
Mountain;
Auditory Periphery |
115 |
|
A typical mammalian
cochlea will have around 2000 IHC's. |
|
0 |
Alexander;
Basal Ganglia |
139 |
|
Basal Ganglia |
|
24 |
Alexander;
Basal Ganglia |
139 |
|
Basal ganglia
include the striatum (comprising the putamen, caudate
nucleus, and ventral striatum), the globus
pallidus,
the substantia nigra, and the subthalamic nucleus. |
|
0 |
Alexander;
Basal Ganglia |
139 |
|
All of the basal ganglia structures are functionally subdivided into skeletomotor, oculomotor, associative, and limbic territories based on
their physiological properties and on their interconnections with cortical and thalamic territories having the same functions. |
|
0 |
Alexander;
Basal Ganglia |
139 |
|
The large-scale
organization of the basal
ganglia can be viewed as a family of reentrant loops that are organized in parallel, each taking its origin from a particular set of functionally related cortical
fields,
passing through the functionally
corresponding portions of the basal ganglia, and returning to
parts of those same cortical fields by way of specific basal ganglia recipient zones in the dorsal thalamus. |
|
0 |
Alexander;
Basal Ganglia |
140 |
|
Owing to the segregation that is maintained along each of the cortical-basal ganglia-thalamocortical circuits, there is little direct communication among
the separate functional domains. |
|
1 |
Alexander;
Basal Ganglia |
140 |
|
Virtually the entire cortical mantle is topologically mapped onto the striatum, which represent the "input" portion of the basal ganglia. |
|
0 |
Alexander;
Basal Ganglia |
140 |
|
The cortically directed signals that emerge from the basal ganglia's output nuclei (internal pallidum and substantia nigra) are returned
exclusively to foci within the frontal lobe, after first passing
through select portions of the thalamus. |
|
0 |
Alexander;
Basal Ganglia |
140 |
|
The mapping of cortical striatal projections is such that functionally interconnected cortical fields (e.g. frontal motor and premotor areas and parietal somatosensory areas) tend
to project to overlapping or contiguous domains within the striatum. |
|
0 |
Alexander;
Basal Ganglia |
140 |
|
Like the cerebellum, the skeletal motor portions of the basal ganglia receive inputs from most of the sensorimotor territories of the cerebral cortex, including primary and secondary somatosensory areas, primary motor
cortex,
and a variety of pre-motor areas, including the supplementary
motor area, the dorsal and ventral premotor areas, and the cingulate
motor areas. |
|
0 |
Alexander;
Basal Ganglia |
142 |
|
Dopaminergic
input to
the putamen consists
of nigrostriatal
projections that originate in the SNc. |
|
2 |
Alexander;
Basal Ganglia |
142 |
|
The cortical motor and premotor areas receive separate dopaminergic projections from the ventral tegmental area. |
|
0 |
Alexander;
Basal Ganglia |
142 |
|
Dopamine
has been shown to have a role in synaptic
plasticity within the striatum, being implicated in
both long term potentiation (LTP) and long-term depression (LTD). |
|
0 |
Alexander;
Basal Ganglia |
142 |
|
Research findings suggest that dopamine neurons may play an important role in determining
when striatal
synapses should be strengthened or weakened. |
|
0 |
Alexander;
Basal Ganglia |
142 |
|
With their combined voltage dependency and ligand specificity, NMDA receptors are widely assumed
to play a role in at least one form of activity-dependent
synaptic plasticity, i.e. LTP. |
|
0 |
Alexander;
Basal Ganglia |
142 |
|
Disinhibition
plays in important role in many current models of basal ganglia operation. |
|
0 |
Bartha; Cerebellum and Conditioning |
169 |
|
Cerebellum and Conditioning |
|
27 |
Bartha; Cerebellum and Conditioning |
169 |
|
The conditioning
stimulus (CS) is usually a tone or
light, and the unconditioned
stimulus (US) is usually a corneal air puff or shock. After repeated pairings of the CS followed by the US, animals
eventually learn to blink in response to the CS, and this is called a conditioned response (CR). |
|
0 |
Bartha; Cerebellum and Conditioning |
169 |
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Blink conditioning can occur with CS-US interstimulus intervals (i.e.
ISIs), ranging from 100 ms to well over 1 second. |
|
0 |
Bartha; Cerebellum and Conditioning |
169 |
|
The US and CS
information must have pathways that bring them together for associative learning of the CR to occur. |
|
0 |
Bartha; Cerebellum and Conditioning |
170 |
|
Evidence points to the cerebellum as the site of convergence and to the olivary climbing fiber
system as the essential
reinforcing pathway. |
|
1 |
Bartha; Cerebellum and Conditioning |
170 |
|
The hippocampus appears to be important for long
ISI trace conditioning (>500
ms) but not for delay conditioning or short ISI trace conditioning. |
|
0 |
Kawato; Cerebellum and Motor Control |
172 |
|
Cerebellum and Motor Control |
|
2 |
Kawato; Cerebellum and Motor Control |
172 |
|
Fast, smooth, and coordinated
movements in humans and animals cannot be achieved by a feedback control
alone because delays associated with feedback loops are long (about 200 ms
for visual feedback and 100 ms for somatosensory feedback). |
|
0 |
Kawato; Cerebellum and Motor Control |
172 |
|
Feedforward control explicitly
incorporates predictive internal models and appears to be essential at least
for relatively fast movements. |
|
0 |
Kawato; Cerebellum and Motor Control |
173 |
|
A number of physiological
studies have suggested important functional roles of the cerebellum in motor
learning and remarkable synaptic plasticity in cerebellar cortex. |
|
1 |
Kawato; Cerebellum and Motor Control |
173 |
|
Biological objects of motor
control by the brain, such as arms, speech articulators, and the torso,
possess many degrees of freedom and complicated nonlinear dynamics. |
|
0 |
Kawato; Cerebellum and Motor Control |
173 |
|
Neural internal models should
receive a broad range of sensory inputs and possess a capacity high enough to
approximate complex dynamics. |
|
0 |
Kawato; Cerebellum and Motor Control |
173 |
|
Extensive sensory signals
carried by mossy fiber inputs and an enormous number of granule cells in the
cerebellar cortex seem to fulfill the prerequisites for neural internal
models. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Dendritic Processing |
|
109 |
Segev; Dendritic Processing |
282 |
|
Dendrites
tend to ramify,
creating large and complicated trees. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Dendrites
are thin processes,
starting with a diameter of a few microns near the soma, and decreasing to less than 1 µ as they successively branch. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Many (but
not all) types of dendrites are studded with dendritic spines. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Dendritic spines are the major postsynaptic target for excitatory synaptic inputs. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Synapses
are not randomly distributed over the dendritic surface. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Inhibitory
synapses are more proximal than excitatory synapses. |
|
0 |
Segev; Dendritic Processing |
282 |
|
Synaptic inputs, both excitatory
and inhibitory, typically operate by locally changing the conductance of the
postsynaptic membrane (opening specific ion channels). |
|
0 |
Segev; Dendritic Processing |
282 |
|
Dendrites
are inherently nonlinear devices. |
|
0 |
Segev; Dendritic Processing |
282 |
|
The fast excitatory (non-NMDA) and inhibitory
(GABAA) inputs operate on a time scale of 1 ms. |
|
0 |
Segev; Dendritic Processing |
282 |
|
The slow excitatory (NMDA) an inhibitory GABAB) inputs both act on a time scale of 10-100 ms. |
|
0 |
Holmes; Dendritic Spines |
289 |
|
Dendritic Spines |
|
7 |
Holmes; Dendritic Spines |
289 |
|
Attempts have been made to
classify spines based on their size, shape, and dendritic location. |
|
0 |
Holmes; Dendritic Spines |
289 |
|
Some researchers have classified spines as stubby, mushroom
shaped,
or long thin. |
|
0 |
Holmes; Dendritic Spines |
289 |
|
Stubby
spines were most numerous in proximal regions, long thin spines dominated distal regions, and mushroom-shaped spines were distributed almost uniformly. |
|
0 |
Holmes; Dendritic Spines |
289 |
|
The spine
categories are
arbitrary,
sense spine shape varies continuously, and all types of spines are found in all areas. |
|
0 |
Holmes; Dendritic Spines |
290 |
|
It does not
appear that the function
of spines can be explained by changes in passive
electrical properties caused by changes in spine dimensions. |
|
1 |
Holmes; Dendritic Spines |
291 |
|
Spines, by restricting the diffusion of substances away from the synapse, may provide a local isolated environment
in which specific
biochemical reactions can occur. |
|
1 |
Holmes; Dendritic Spines |
291 |
|
The spine
stem may provide a diffusional resistance that allows
calcium to become concentrated in the spine head and calcium
dependent reactions to be localized to the synapse. |
|
0 |
Holmes; Dendritic Spines |
291 |
|
The diffusional
resistance of spines could be very important for plasticity
changes,
such as those that occur with long term
potentiation. |
|
0 |
von der
Malsburg; Dynamic Link Architecture |
329 |
|
Dynamic Link Architecture |
|
38 |
Lopes da
Silva; EEG Analysis |
348 |
|
EEG Analysis |
|
19 |
Lopes da
Silva; EEG Analysis |
348 |
|
The
electroencephalogram, or EEG, consists of the electrical activity of relatively large neuronal
populations that can be recorded from the scalp. |
|
0 |
Lopes
da Silva; EEG Analysis |
348 |
|
A constant preoccupation of
electroencephalographic research has been to develop techniques to extract
information from signals, recorded at the scalp, that may be relevant for the
diagnosis of brain diseases, and to obtain a better understanding of the brain
processes underlying psychophysical and cognitive functions. |
|
0 |
Lopes da
Silva; EEG Analysis |
348 |
|
Even when the subject's
behavioral state is almost constant, the duration of epochs that have the
same statistical properties, i.e. are stationary, is usually short. |
|
0 |
Lopes da
Silva; EEG Analysis |
348 |
|
EEG signals
present essential nonstationary properties. |
|
0 |
Lopes da
Silva; EEG Analysis |
349 |
|
The degree of relationship between two EEG signals can be estimated using the cross-correlation function. |
|
1 |
Lopes da
Silva; EEG Analysis |
350 |
|
EEG signals
are distributed not
only in time but also in space. |
|
1 |
Lopes da
Silva; EEG Analysis |
350 |
|
To account for spatial distributions with the steepest spatial gradients, it is necessary to have inter-electrode distances of at most 2.5
cm,
which corresponds to recording from about 128
electrodes placed on the scalp. |
|
0 |
Lopes da
Silva; EEG Analysis |
351 |
|
EEG recordings are complex
signals that may provide valuable information about the underlying brain
systems, since they have unsurpassed resolution in time, although their
spatial resolution is quite limited. |
|
1 |
LeDoux; Emotion and Neuroscience |
356 |
|
Emotion and Computational
Neuroscience |
|
5 |
LeDoux; Emotion and Neuroscience |
|
|
William James's famous question: Do we run from
the bear because we are afraid, or are we afraid because we run? |
|
-356 |
LeDoux; Emotion and Neuroscience |
357 |
|
The disparate theories of
emotional experience all point to a common mechanism -- a central
evaluative system that determines whether a given situation is potentially harmful or beneficial to the individual. |
|
357 |
LeDoux; Emotion and Neuroscience |
357 |
|
Since the evaluation
mechanisms are precursors to conscious emotional
experiences, they must be unconscious processes. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Many emotional
reactions are likely to involve unconscious information processing of stimulus significance, with the experience
of emotion (the subjective state of fear) coming after
the fact. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Traditionally, emotion has been ascribed to the brain's limbic system, which is presumed to be an evolutionarily old part of the brain involved in the survival of the individual and species. (MacLean, 1952) |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
The persistence of the limbic system theory of emotion is due in large part to the fact that the amygdala, a small region in the temporal lobe, was included in the
concept. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Lesions of
the amygdala region interfere with both positive and negative emotional reactions. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Experimental studies show that cells in the amygdala are sensitive to the rewarding and punishing features of stimuli, and to the social
implications of stimuli. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
The contribution of the amygdala to emotion results in large
part from its anatomical connectivity. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
The amygdala receives inputs from each of the major sensory
systems and from higher-order
association areas of
the cortex. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Sensory inputs to the amygdala arise from both the thalamic and cortical levels. |
|
0 |
LeDoux;
Emotion and Neuroscience |
357 |
|
The various
inputs to the amygdala allow a variety of levels of information
representation (from raw sensory features processed in
the thalamus, to whole
objects processed in sensory
cortex,
to complex scenes or contexts processed in the hippocampus) to impact on the amygdala and thereby activate emotional reactions. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
All of the inputs to the amygdala converge on the lateral nucleus, which can be viewed
as the sensory and cognitive gateway into the amygdala's
emotional functions. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
The amygdala sends output projections to a variety of brain stem systems involved in controlling emotional responses, such as species-typical behavioral (including
facial) responses, autonomic
nervous system responses, and endocrine responses. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
All of the outputs of the amygdala originate from the central
nucleus. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Within the amygdala, the stimulus input region (lateral nucleus) and the motor output region (central
nucleus) are interconnected, allowing an anatomical account of how events in the world come to elicit emotional responses. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Much of the anatomical
circuitry of emotion has been elucidated through studies of fear conditioning, a procedure whereby an emotionally
neutral stimulus,
such as a tone or light, is associated with an aversive event, such as a mild footshot. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
After conditioned pairing, the
tone or light comes to elicit emotional reactions that characteristically
expressed when members of the species in question are threatened. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Although many emotional response patterns are hardwired in the brain circuitry, the particular stimulus conditions that activate these are mostly learned
by association through classical conditioning. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
The amygdala appears to
contribute significantly to learning and memory and may be a crucial site for
synaptic plasticity in emotional learning. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Emotional memory is quite
different from declarative memory, the ability to consciously recall some
experience from the past. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
Declarative memory, in contrast to the emotional memory, crucially requires the hippocampus and related areas of the cortex. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
In order to give declarative memory an emotional flavor, it may be necessary for the stimulus to activate the emotional memory system of the amygdala. |
|
0 |
LeDoux; Emotion and Neuroscience |
357 |
|
It is likely the dual activation of the declarative and emotional memory systems that gives our ongoing declarative
memories their emotional coloration. |
|
0 |
LeDoux; Emotion and Neuroscience |
358 |
|
Computers
are now being used as tools for modeling certain aspects of emotional processing. |
|
1 |
LeDoux; Emotion and Neuroscience |
358 |
|
Conditioned reinforcement
involves pairs of antagonistic neural processes, such as fear and relief. |
|
0 |
LeDoux; Emotion and Neuroscience |
358 |
|
Computer simulations show and possibly explain various effects related to the acquisition and extinction of conditioned excitation and inhibition. |
|
0 |
LeDoux; Emotion and Neuroscience |
358 |
|
Hippocampo-amygdaladoid system, described as a zone
of convergence of conditioned (CS) and
unconditioned (US) stimulus pathways. |
|
0 |
LeDoux; Emotion and Neuroscience |
358 |
|
Researchers have long recognized
the mutual influences of emotions and cognition. |
|
0 |
LeDoux;
Emotion and Neuroscience |
358 |
|
Evaluative
or appraisal
processes
function by comparing sensed characteristics of the world to internal goals, standards, and attitude structures, deducing the emotional significance of the stimulus, guiding the expression of emotional behavior and other physiological responses, and influencing other modules pertaining to behavioral
decisions. |
|
0 |
LeDoux; Emotion and Neuroscience |
358 |
|
Mapping between appraisal
features (e.g. novelty, urgency, intrinsic pleasantness) and emotional categories (e.g. fear, joy, pride). |
|
0 |
LeDoux; Emotion and Neuroscience |
359 |
|
Appraisal
of sensory information might
be one of the most prominent functions of the amygdala. |
|
1 |
LeDoux; Emotion and Neuroscience |
359 |
|
The existence of multiple pathways to the amygdala from input processing systems
provides biological
corroboration for the importance
of cognition in driving emotion. |
|
0 |
LeDoux; Emotion and Neuroscience |
359 |
|
The expression of emotion in the face is an important biological aspect of emotion that has significant
implications for how emotion is communicated in social situations. |
|
0 |
LeDoux; Emotion and Neuroscience |
359 |
|
Research studies have shown cells selectively responsive to particular faces in areas of temporal neocortex and in the amygdala. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Emotion-Cognition Interactions |
|
1 |
Beeman; Emotion-Cognition |
360 |
|
Mere exposure to a stimulus causes subjects to show increased positive affect toward the stimulus, even if they are not aware of having previously encountered
it. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
The mere
exposure effect precludes the involvement of conscious cognition, but not of unconscious cognition. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Some human
emotions involved little
or no conscious cognition, while others (e.g. pride, relief) certainly require considerable
cognition,
much of it conscious. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Cognition
often plays an important role in elicitation and intensification of emotions. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Neuroanatomical studies in vertebrates implicate the amygdala in affective processing of sensory stimuli. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
The amygdala receives inputs
directly from the thalamus, including from thalamic sensory nuclei, and
indirectly from the cortex. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Amygdala
inputs from the thalamus may allow fast but simple
processing of affective aspects of sensory stimuli. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Amygdala
inputs received indirectly
from the cortex allow slower but fuller (cognitive) processing of stimulus affect. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
The mere
exposure effect occurs
without conscious cognition, presumably through direct thalamo-amygdaloid afferents. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
In categorizing and learning, detection of novelty is more
closely related to cognitive processing and the cortex than to emotional processing and the amygdala. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Associating
initially neutral stimuli with emotional
responses may be a core component of emotion. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Neural network models that simulated various aspects of classical
conditioning. |
|
0 |
Beeman; Emotion-Cognition |
360 |
|
Neural network models have simulated reward-mediated learning of behavior, which allows emotional systems to relate so-called hardwired emotional responses to a
variety of stimuli. |
|
0 |
Beeman; Emotion-Cognition |
361 |
|
Brain structures involved in emotional processing, such as the amygdala, receive input from many cortical areas. |
|
1 |
Beeman; Emotion-Cognition |
361 |
|
Cortical inputs to emotional processing come from most input modalities, but more from secondary and tertiary sensory areas than from primary areas. |
|
0 |
Beeman; Emotion-Cognition |
361 |
|
Table of emotion types with a
representative emotion words (table) |
|
0 |
Beeman; Emotion-Cognition |
362 |
|
Influence
of the Emotion on Cognition |
|
1 |
Beeman; Emotion-Cognition |
362 |
|
Neural network models of emotion processing and of emotion-cognition
interactions have
concentrated on the contribution of emotion
processing to learning and memory. |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
Grasping Movements: Visuomotor
Transformations |
|
76 |
Rizzolatti;
Grasping Movements |
438 |
|
The parietal
lobe of primates consists of three main sectors: the postcentral gyrus, the superior
parietal lobules, and the
inferior parietal lobules (IPL). |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
Visual inputs to the IPL arrived
from the occipitotemporal areas DP, PO, MT, MST, V3A, the areas located in
the anteriosuperior temporal sulcus, and from the visual periphery of V3 and
V2. |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
Physiological studies have shown
that each of the subdivisions
of the IPL are involved in specific sensorimotor transformations. |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
The IPL can be conceived as a set of largely
independent modules, each of which is responsible for the organization of a specific motor action. |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
The IPL
motor actions include: saccadic eye
movements,
ocular fixation, reaching, and grasping. |
|
0 |
Rizzolatti;
Grasping Movements |
438 |
|
A modular organization similar to
that of the parietal lobe is present also in the motor
sector of the frontal lobe. |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
Some interesting principles of cellular encoding of complex actions have begun to emerge. |
|
2 |
Rizzolatti;
Grasping Movements |
440 |
|
Motor
information, as well as visual information for action, is centrally
coded in abstract
terms. |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
A motor act
is coded in terms of its goal ("grasp,"
"hold"), regardless of whether the goal is to be achieved by the muscles of the right hand, left hand, or even the mouth. |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
Information
for complex acts is compressed in populations of specific units |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
Grip movements are not
organized on the basis of a direct link between visual
neurons and motor
neurons controlling individual
muscles or simple
finger movements. |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
General commands (e.g. grasp with the mouth or hands) are often not distinct from a precise
specification of how action has to be performed (e.g.
precision grip). |
|
0 |
Rizzolatti;
Grasping Movements |
440 |
|
It appears that what to do and how to do it are processed cojointly. |
|
0 |
Wang; Habituation |
441 |
|
Habituation |
|
1 |
Wang; Habituation |
441 |
|
Habituation is a decrease in
the strength
of
a behavioral response that occurs when an initially novel stimulus is presented repeatedly. |
|
0 |
Wang; Habituation |
441 |
|
Habituation
is probably the most elementary and ubiquitous form of plasticity, and its underlying mechanisms may provide
the basis for understanding other forms of
plasticity and more
complex learning behaviors. |
|
0 |
Wang; Habituation |
441 |
|
Habituation
can be distinguished from other types
of behavioral decrement like fatigue. |
|
0 |
Wang; Habituation |
442 |
|
The orienting
reflex in higher
vertebrates is subject to habituation on repeated presentation. |
|
1 |
Calabrese;
Half-Center Oscillators |
444 |
|
Half-Center Oscillators
Underlying Rhythmic Movements |
|
2 |
Calabrese;
Half-Center Oscillators |
444 |
|
Most rhythmic
movements, including locomotive
movements, are programed in part by central pattern generating networks that comprise neural oscillators. |
|
0 |
Calabrese;
Half-Center Oscillators |
444 |
|
In many of the pattern generating networks, in
both vertebrates and invertebrates, reciprocal
inhibitory synaptic interactions between neurons or groups of neurons are thought to
form the basis of oscillation in the network. |
|
0 |
Calabrese;
Half-Center Oscillators |
446 |
|
A general theoretical framework
for understanding how reciprocally inhibitory neurons
oscillate was recently developed |
|
2 |
Brown; Hebbian Plasticity |
454 |
|
Hebbian Synaptic Plasticity |
|
8 |
Brown; Hebbian Plasticity |
455 |
|
Although a Hebbian
mechanism uses only local information, the
modification process may be subject to global
control signals. |
|
1 |
Brown; Hebbian Plasticity |
455 |
|
A global
reinforcement signal can control Hebbian plasticity in a large population of activated synapses. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
The global
modulation in Hebbian
plasticity is different from an external
"teacher" signal that explicitly "instructs" selective
modification on a synapse-by-synapse basis, independent of local activity. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
Whether a change occurs at a Hebbian synapse depends on activity on both sides of the synaptic cleft. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
The interactive
requirement for both presynaptic and postsynaptic activity makes the Hebbian plasticity mechanism fundamentally associative. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
Modifications
in a Hebbian synapse
depend on the exact time of occurrence of pre-
and postsynaptic activity. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
Only temporally overlapping pairing of two inputs produce associative LTP. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
A Hebbian
synapse may be defined as one that uses a time-dependent,
highly local, and strongly
interactive mechanism to increase synaptic efficacy as a function of the conjunction or correlation between pre- and postsynaptic activity. |
|
0 |
Brown; Hebbian Plasticity |
455 |
|
The form of
LTP that has been most extensively
studied is the variety that occurs in the Schaffer collateral synaptic input to the pyramidal neurons of hippocampal region CA1. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
Glutamate,
the major excitatory neurotransmitter in the hippocampus, exerts its action through at least two major classes of receptors -- NMDA
and non-NMDA. |
|
1 |
Brown; Hebbian Plasticity |
456 |
|
The non-NMDA
category of receptors
can be further subdivided into at least two classes -- the AMPA receptor and metabotropic receptor (a second messenger-coupled
receptor). |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
The fast
component of the excitatory
postsynaptic current (EPSC) at the synapses in the CA1 region is mediated primarily
via the AMPA receptors, while the slow
component is mediated by NMDA receptors. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
The fast AMPA-receptor component lasts a few milliseconds and has a fast rise time, and its duration is limited by the time constant of the cell membrane. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
The slow NMDA component has a slower rise time (~20 ms) and a longer
decaying phase (about 200-300
ms). |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
The induction of the most commonly studied form of LTP at the Schaffer collateral synapses is
controlled by the NMDA
subclass of glutamate receptor. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
The common
working hypothesis is that the Ca2+ influx through the NMDA receptor-gated channel and the resultant increase in
postsynaptic Ca2+ are partly responsible for triggering
the induction of LTP. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
How does Ca2+ influx through the NMDA receptor gated
channel relate to the Hebbian
nature of LTP
induction? It turns out that this channel has just the kind of gating properties
needed for a Hebb-type conjunctive mechanism. |
|
0 |
Brown; Hebbian Plasticity |
456 |
|
Just as they are many ways of implementing a Hebbian algorithm theoretically, nature may have more than one way of configuring a Hebbian synapse. |
|
0 |
Brown; Hebbian Plasticity |
459 |
|
Hebbian Synaptic Plasticity |
|
3 |
Brown; Hebbian Plasticity |
459 |
|
Hebb's theory was based on an
entirely new concept, that of "cell assembly," an activity process
that reverberates in "a set of closed pathways." |
|
0 |
Brown; Hebbian Plasticity |
459 |
|
Hebbian synapses introduced a way of reinforcing
coupling between coactive
cells and thus of growing
assemblies. |
|
0 |
Brown; Hebbian Plasticity |
459 |
|
Hebb
developed similar hypotheses for a higher
hierarchical level of
organization, which allow the linking between cognitive events and their recall as a temporally
organized series of activations of assemblies. Hebb referred to
this binding process as a "phase
sequence." |
|
0 |
Brown; Hebbian Plasticity |
459 |
|
A period of
maintained temporal correlation between pre- and postsynaptic activity will lead to an increase in the efficacy of synaptic transmission. |
|
0 |
Brown; Hebbian Plasticity |
459 |
|
The postulated fast binding process during visual
shape recognition depending on temporal correlation of firing between costimulated cells, the hypothesis of "fast Hebbian synapses," offered a new field of validation for Hebbian associative theories on
the millisecond time scale. |
|
0 |
Brown; Hebbian Plasticity |
460 |
|
Most algorithms of synaptic plasticity use rules of normalization that require depression of certain synapses in addition to Hebbian reinforcement of active connections -- some assume forgetting mechanisms slowly
activated by disuse, or complementary
plasticity rules operating at the level of synapses fed by the active
pathway, and by neighboring inactive afferents. |
|
1 |
Brown; Hebbian Plasticity |
460 |
|
The synaptic plasticity algorithms
predict spatial and temporal competition between active fibers, which impinge on a common target cell. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
Hippocampus Spatial Models |
|
8 |
Burgess;
Hippocampus Spatial Models |
468 |
|
The hippocampus is one of the most studied areas of the brain. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
The hippocampus is many synapses removed from sensory transducers or motor effectors. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
The hippocampus has been studied for its role in neurological
disorders, including epilepsy,
schizophrenia, and
Alzheimer's disease. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
Bilateral damage to the hippocampus and nearby structures in patient HM, intended as treatment
for epilepsy,
produced a profound retrograde and anterograde amnesia. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
The hippocampus has become the primary region in the mammalian brain for the study of the synaptic
basis of memory and learning. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
Structurally,
the hippocampus is
the simplest form of cortex. |
|
0 |
Burgess;
Hippocampus Spatial Models |
468 |
|
The hippocampus contains one projection cell type, which is confined to a single layer, and it receives
inputs from all sensory systems and association cortex areas. |
|
0 |
Burgess;
Hippocampus Spatial Models |
469 |
|
Marr (1971) proposed
a theory for how the hippocampus could function as an associative
memory. From this proposal have followed many extensions, usually focusing
on the role of the CA3 recurrent collaterals. |
|
1 |
Burgess;
Hippocampus Spatial Models |
469 |
|
Single-unit recordings in freely
moving rats have revealed place cells (PCs), in fields CA3 and CA1 of the
hippocampus. |
|
0 |
Burgess;
Hippocampus Spatial Models |
469 |
|
Cells in the entorhinal
cortex (the main
cortical input to the hippocampus) also have spatially correlated firing, but
tend to have larger, less well-defined place
fields than those in CA3 or CA1. |
|
0 |
Burgess;
Hippocampus Spatial Models |
469 |
|
The
electroencephalogram (EEG) recorded in the hippocampus is the largest electrical signal in the brain. |
|
0 |
Burgess;
Hippocampus Spatial Models |
469 |
|
One form of EEG, call the theta rhythm, is a sinusoidal oscillation of 7-12
Hz. |
|
0 |
Lesher; Illusory Contour Formation |
481 |
|
Illusory Contour Formation |
|
12 |
Lesher; Illusory Contour Formation |
481 |
|
Illusory contours (ICs) --
defined as the percept of clear boundaries in regions with no corresponding
luminance gradient. |
|
0 |
Lesher; Illusory Contour Formation |
481 |
|
Eponymous figures, which exhibit
three important characteristics -- (1) a sharp illusory contour delineating
the border of the figure, (2) an illusory brightening of the figure with
respect to its background, and (3) the appearance that the figure is floating
above the inducing stimuli. |
|
0 |
Lesher; Illusory Contour Formation |
481 |
|
Gestalt theory of illusory
contours stresses
that autonomous processes for visual
grouping,
perhaps influenced by, but different from cognitive factors, discern the
"wholes" in visual
stimulation. |
|
0 |
Intrator;
Information Theory Plasticity |
484 |
|
Information Theory and Visual
Plasticity |
|
3 |
Intrator;
Information Theory Plasticity |
484 |
|
A general class of learning
rules stems from the information theoretic idea of maximizing the mutual information between the network's output and
input, or as it is sometimes called, minimizing the information loss across the network. |
|
0 |
Intrator;
Information Theory Plasticity |
484 |
|
Shannon
considered information
as a loss in the uncertainty. |
|
0 |
Intrator;
Information Theory Plasticity |
484 |
|
Shannon
considered the problem of information flow through a noisy communications channel, seeking to optimize the code so
is to send the smallest number of bits on average while still achieving reliable communication. |
|
0 |
Intrator;
Information Theory Plasticity |
484 |
|
The mutual
information maximization principle formulates Shannon's ideas. |
|
0 |
Byrne; Invertebrate Learning: Aplysia |
487 |
|
Invertebrate Models of Learning:
Aplysia |
|
3 |
Byrne; Invertebrate Learning: Aplysia |
487 |
|
The siphon-gill
and tail-siphon
withdrawal reflexes of Aplysia have been used to analyze the neuronal
mechanisms contributing to nonassociative and associative learning. |
|
0 |
Byrne; Invertebrate Learning: Aplysia |
487 |
|
Habituation
refers to a response
decrement as a result
of repeatedly delivering a stimulus |
|
0 |
Byrne; Invertebrate Learning: Aplysia |
487 |
|
Sensitization
refers to an enhancement of response by the repeated delivery of stimuli. |
|
0 |
Byrne; Invertebrate Learning: Aplysia |
489 |
|
Associative learning, classical
conditioning, Pavlovian conditioning. |
|
2 |
Bargas; Ion Channels |
496 |
|
Ion Channels: Keys to Neuronal
Specialization |
|
7 |
Bargas; Ion Channels |
496 |
|
Voltage spikes, or impulses
called action potentials (APs), are the major means by which neurons code
information and communicate via their axons. |
|
0 |
Bargas; Ion Channels |
496 |
|
Ion conductance diversity in a single type of neuron explains intrinsic firing properties. |
|
0 |
Bargas; Ion Channels |
496 |
|
Ion conductance diversity between different types of neurons explains the functional classes of neurons found in the brain. |
|
0 |
Bargas; Ion Channels |
496 |
|
Some neurons fire spontaneously, some show adaptation, some fire in
bursts, etc. |
|
0 |
Bargas; Ion Channels |
496 |
|
Neurons differ in morphology and
function (diagram) |
|
0 |
Bargas; Ion Channels |
496 |
|
If neurons show different firing
properties (i.e. threshold, adaptation, pacemaking, bursting, etc.), how does
the firing property relate to the class of information processing a given
nucleus performs? |
|
0 |
Plunkett;
Language Acquisition |
503 |
|
Language Acquisition |
|
7 |
Plunkett;
Language Acquisition |
504 |
|
One of the most widely cited
facts of early language acquisition concerns the dramatic change in rate of vocabulary growth observed in many
children around 21 months of age -- the so-called vocabulary spurt. |
|
1 |
Plunkett;
Language Acquisition |
505 |
|
The complexity of structural relations in language, such as long-distance
dependencies. |
|
1 |
Plunkett;
Language Acquisition |
505 |
|
Recurrent networks can learn long-distance dependencies. |
|
0 |
Thorpe; Localized Versus Distributed |
549 |
|
Localized
Versus Distributed
Representation |
|
44 |
Thorpe;
Localized Versus Distributed |
549 |
|
What happens in the brain when a
person recognizes a familiar stimulus such is the face
of the person' s grandmother? Most researchers accept that the act of perception involves the activation of some form of internal representation, but there
is little agreement about how such representations are implemented in neuronal circuitry. |
|
0 |
Thorpe; Localized Versus Distributed |
550 |
|
Many scientists have argued in
favor of distributed coding. The list includes Donald Hebb, Karl Lashley, and Gerald Edelman. |
|
1 |
Thorpe; Localized Versus Distributed |
550 |
|
The so-called Hopfield net is a clear example of distributed
encoding,
since all units
are involved in
representing
all of the various input patterns. |
|
0 |
Dorman; Motivation |
591 |
|
Motivation |
|
41 |
Dorman; Motivation |
591 |
|
Motivated behavior is usually goal oriented -- associated with a drive, such as hunger or thirst (called primary motivation); or prompted by prior learning (called secondary motivation), which
typically elicits more complex behaviors. |
|
0 |
Dorman; Motivation |
591 |
|
The concept of motivation is related to, but distinct from, other concepts such
as instincts,
derives, and reflexes. |
|
0 |
Dean; Motor Pattern Generation |
600 |
|
Motor Pattern
Generation |
|
9 |
Dean; Motor Pattern Generation |
601 |
|
Hierarchical description of motor pattern generation, showing interactions between efferent, afferent, and central elements (diagram) |
|
1 |
Dean; Motor Pattern Generation |
601 |
|
The constraints of biological evolution means that
each biological motor
pattern generator (MPG)
is one which was adequate and possible given the components of the animal's
ancestors. |
|
0 |
Dean; Motor Pattern Generation |
602 |
|
Central pattern generator (CPG) -- a set of elements in the CNS able, in the absence of patterned sensory input, to produce patterned
motor output related to natural behaviors. |
|
1 |
Dean; Motor Pattern Generation |
602 |
|
MPG's
incorporate both central and peripheral elements. Motor patterns represent an interaction between CNS activity (CPGs)
and peripheral biomechanical faculties. |
|
0 |
Schuz; Neuroanatomy Computational |
622 |
|
Neuroanatomy in a Computational
Perspective |
|
20 |
Schuz; Neuroanatomy Computational |
623 |
|
Different types of geometry in
the gray matter (diagram) |
|
1 |
Schuz; Neuroanatomy Computational |
623 |
|
The brain as a whole is highly structured. Some parts interact directly with each other by way of reciprocal connections, such as the cerebral cortex and the thalamus. Other parts may be arranged in loops. |
|
0 |
Schuz; Neuroanatomy Computational |
623 |
|
Depending on the route a signal takes, it can be fed back either negatively or positively onto its parent
structure. |
|
0 |
Schuz; Neuroanatomy Computational |
623 |
|
Positive feedback is sometimes transmitted by way of disinhibition, went two inhibitory stations are connected in series. This type of positive feedback characterizes the corticocortical
loop through the basal
ganglia. |
|
0 |
Cliff; Neuroethology, Computational |
626 |
|
Neuroethology, Computational |
|
3 |
Cliff; Neuroethology, Computational |
626 |
|
Neuroethology--
the intersection of neuroscience (the study of the nervous system) and ethology (the study of animal behavior). |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA Receptors: Synaptic,
Cellular, and Network Models. |
|
18 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptors are subtypes of receptors for the excitatory
neurotransmitter glutamate. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptors mediate a relatively slow excitatory postsynaptic potential (EPSP). |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptor activation
requires not only the binding of an agonist, but also the depolarization of
the postsynaptic membrane to remove the voltage-dependent blockade of the
channel by Mg2+
ions. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptors act as coincidence detectors of presynaptic and postsynaptic activity. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptors have attracted a great deal of interest in neuroscience
because of their role in learning and memory. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
The
coincidence detector property of NMDA receptors make them an ideal molecular
device for producing
Hebbian synapses, i.e. synapses whose strength is modified depending on the correlation of presynaptic and postsynaptic activity. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
The influx of Ca2+ ions through
NMDA receptor
channels triggers a cascade
of molecular processes that leads to various
forms of synaptic plasticity, including short-term
potentiation (STP), long term
potentiation (LTP), and long term
depression. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
NMDA receptor activation requires not only the
presence of glutamate
but also the presence of another amino acid, glysine, which is called a co-agonist. |
|
0 |
Liaw; NMDA Receptors |
644 |
|
As with most ligand
gated ion channels, a major characteristic of the
NMDA receptor is the
existence of a rapid desensitization (a
long period of inactivation) after agonist-induced activation. |
|
0 |
Liaw; NMDA Receptors |
646 |
|
In addition to synaptic plasticity, NMDA receptors are also involved
in the integration of
spatiotemporal patterns
of inputs to a neuron. |
|
2 |
Liaw; NMDA Receptors |
647 |
|
Researchers developed a computer model of hippocampal region CA3 that
consisted of pyramidal neurons and inhibitory interneurons. |
|
1 |
Liaw; NMDA Receptors |
647 |
|
The three
unique properties NMDA receptors (i.e. slow conductance, voltage and transmitter dependency,
and calcium permeability) provide the basis for their involvement in STP, LTP, synaptic integration, motor pattern generation, and epileptiform activity. |
|
0 |
Wilson; Olfactory Cortex |
669 |
|
Olfactory Cortex |
|
22 |
Wilson; Olfactory Cortex |
669 |
|
Olfactory cortex has his simplest organization among the main types of cerebral cortex. |
|
0 |
Wilson; Olfactory Cortex |
669 |
|
Olfactory cortex may serve as a model for understanding basic
principles underlying cortical
organization. |
|
0 |
Wilson; Olfactory Cortex |
669 |
|
The cerebral
cortex first appears
in vertebrate evolution
in a fish as a simple
structure composed of three layers: a superficial layer containing incoming nerve fibers, dendrites of intrinsic and output neurons, and scattered cell bodies of
interneurons; a layer of the large cell bodies of output
neurons;
and a deep layer
of Interspersed input and output fibers and scattered cell bodies of interneurons. |
|
0 |
Wilson; Olfactory Cortex |
670 |
|
The ventrolateral
layer of cortex that receives direct olfactory input from the olfactory bulb is turned paleocortex. |
|
1 |
Wilson; Olfactory Cortex |
670 |
|
On the medial surface is another
part, related to the septum, turned the archicortex; this part is the anlage of the hippocampus in higher vertebrates. |
|
0 |
Wilson; Olfactory Cortex |
670 |
|
On the dorsal surface is the dorsal cortex, generally believed
to be the anlage of
the neocortex. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
The distal inputs of the
olfactory cortex are made exclusively onto dendritic spines of the apical
dendrites. |
|
1 |
Wilson; Olfactory Cortex |
671 |
|
Dendritic spines are small branches, only a few microns in length and 0.1-0.2
µ in diameter, whose head (1-2 µ across) receives the excitatory input
synapses. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
Spines are
of considerable interest as sites for activity-dependent
mechanisms, such as long term potentiation (LTP), that may underlie learning. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
Both the afferent excitatory inputs and the recurrent excitatory inputs are made onto the spines, and both show properties of LTP. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
The intrinsic cortical circuits for processing information consists of
inhibitory and excitatory local circuits. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
The inhibitory
circuits are of two
types: those for feedforward inhibition and those for feedback (lateral) inhibition. |
|
0 |
Wilson; Olfactory Cortex |
671 |
|
Any given interneuron may be involved exclusively in either feedforward or feedback inhibition or it may be a node for convergence and integration of both types. |
|
0 |
Wang; Oscillatory and Bursting |
686 |
|
Oscillatory
and Bursting
Properties of Neurons |
|
15 |
Wang; Oscillatory and Bursting |
686 |
|
Rhythmicity
is a common feature of
temporal organization
in neuronal firing
patterns. |
|
0 |
Wang; Oscillatory and Bursting |
686 |
|
Many stereotypical
single neuron patterns
include a fascinating variety of endogenous oscillations. |
|
0 |
Wang; Oscillatory and Bursting |
686 |
|
Many types
of ion channels are known and some
specific neuronal rhythms have been linked to selected
subsets of channels. |
|
0 |
Wang; Oscillatory and Bursting |
687 |
|
Network synaptic
interactions and dendritic cable properties influence bursting behavior. |
|
1 |
Wang; Oscillatory and Bursting |
687 |
|
The main
biophysical idea is that rhythmicity is generated by a depolarization process that is autocatalytic (positive feedback), followed by
a slower repolarization process (negative feedback). |
|
0 |
Wang; Oscillatory and Bursting |
687 |
|
Burst-like features are seen in
10 Hz oscillations of mammalian thalamic reticular neurons during the spindle
waves of quiet sleep. |
|
0 |
Wang; Oscillatory and Bursting |
687 |
|
The limbic
system's theta rhythm
and gamma fast
oscillations (approximately 40 Hz) occur intermittently with
increased alertness
and focused attention. |
|
0 |
Wang; Oscillatory and Bursting |
687 |
|
Some oscillations depend on the electrical cable
properties of neuronal
dendrites and intracellular sources of regenerative ion fluxes. |
|
0 |
Wang; Oscillatory and Bursting |
688 |
|
Examples of rhythmic
bursting (diagram) |
|
1 |
Buhmann;
Oscillatory Associative |
691 |
|
Oscillatory Associative Memories |
|
3 |
Buhmann;
Oscillatory Associative |
691 |
|
Associative
recall and completion
of information is one of the astonishing abilities of intelligent living beings. |
|
0 |
Buhmann;
Oscillatory Associative |
691 |
|
The basic
principle of associative
information recall is a network dynamic that maps
initial network
states to a subset of final states. |
|
0 |
Devor; Pain Networks |
698 |
|
Pain Networks |
|
7 |
Devor; Pain Networks |
698 |
|
Pain differs from
other sensory systems in the ability of various extrinsic or stimulus-generated, and intrinsic, or central nervous system
(CNS)-generated, state
variables
to modulate the relations between stimulus and felt response. |
|
0 |
Devor; Pain Networks |
698 |
|
On the battlefield for example, it is not that this soldier
overcomes his pain by dint of heroism, but that, temporarily, the pain is simply not felt. |
|
0 |
Devor; Pain Networks |
698 |
|
Perceptual modulation of pain makes good evolutionary sense, since in times of danger it is adapted to turn off pain, even in the
presence of injury, so as not to
impede fight or flight. |
|
0 |
Devor; Pain Networks |
698 |
|
Even under everyday
conditions,
normal rational people display wide person-to-person and trial-to-trial
variability in the amount of pain reported after the administration of noxious stimuli. |
|
0 |
Devor; Pain Networks |
698 |
|
Rather than speaking of pain stimuli (or pain receptors), the preferred
term is noxious stimuli
(or nociceptors)
because the stimuli
may or may not evoke pain. |
|
0 |
Devor; Pain Networks |
698 |
|
Nerve impulses travel from the
receptor ending, past the dorsal root ganglia (DRG), and on into the CNS
without pause. |
|
0 |
Devor; Pain Networks |
698 |
|
As in other sensory systems,
somatosensory primary afferents show modality specificity. |
|
0 |
Devor; Pain Networks |
698 |
|
Low-threshold afferents respond to specific weak stimuli such as touch and vibration. |
|
0 |
Devor; Pain Networks |
698 |
|
Low-threshold
afferents mostly have
heavily myelinated, fast-conducting axons. |
|
0 |
Devor; Pain Networks |
698 |
|
Nociceptors respond only to strong stimuli and either display modality
specificity (e.g. pinch, hot, cold) or are polymodal. |
|
0 |
Devor; Pain Networks |
698 |
|
All nociceptors have slowly conducting axons with thin or
no myelin. |
|
0 |
Devor; Pain Networks |
698 |
|
The firing
frequency of each primary afferent type encodes only a narrow range of stimulus intensities. |
|
0 |
Devor; Pain Networks |
698 |
|
As stimulus
intensity increases from touch to pinch, there is a progressive handoff of encoding function from low threshold afferents to nociceptors. |
|
0 |
Devor; Pain Networks |
698 |
|
The somatosensory
system
codes more than six
decades of discriminable stimulus strengths using neurons that can vary their firing rate over no more than
approximately two decades. |
|
0 |
Devor; Pain Networks |
698 |
|
Normally, stimulation of low-threshold afferents, even at maximum frequencies, evokes a sensation of pressure or rapid vibration, but not pain. |
|
0 |
Devor; Pain Networks |
698 |
|
The Population
Vector Model posits that touch is felt when a few wide dynamic range (WDR) neurons fire
slowly
and pain is
felt when many fire rapidly. |
|
0 |
Devor; Pain Networks |
698 |
|
Precision
encoding over many
decades
as stimulus discriminability is achieved through
the progressive recruitment of WDR neurons that have sequentially overlapping encoding functions. |
|
0 |
Devor; Pain Networks |
698 |
|
Any given stimulus, weak or strong, produces a unique aggregate of impulse activity viewed across the entire population of involved neurons. |
|
0 |
Devor; Pain Networks |
698 |
|
Sensory
modulation in the periphery is obvious from everyday experience, e.g. in
sunburned skin, gentle touch evokes pain. |
|
0 |
Devor; Pain Networks |
698 |
|
Tissue inflammation can increase the gain of nociceptive nerve endings. |
|
0 |
Devor; Pain Networks |
699 |
|
The midbrain
periaqueductal gray matter (PAG) is a nodal
point
for a descending inhibitory control
circuit
through which the brain gates all sending nociceptive information. |
|
1 |
Devor; Pain Networks |
699 |
|
Electrical stimulation of the
periaqueductal gray matter (PAG), all the micro in Jackson mayor of opiates,
activates hindbrain nuclei with cells containing the neurotransmitters
serotonin and norepinephrine. Hindbrain nuclei was cells containing serotonin
on that norepinephrine send impulses along a descending spiral track which
inhibits the response of WB are neurons to notch his inputs. |
|
0 |
Devor; Pain Networks |
699 |
|
The midbrain-medullo-spinal
inhibitory circuit appears to be
largely responsible for the analgesia obtained from systemic injection of morphine and related narcotics. |
|
0 |
Devor; Pain Networks |
699 |
|
Morphine excites PAG neurons, initiating the descending inhibition of noxious inflow from the spinal cord. |
|
0 |
Devor; Pain Networks |
699 |
|
Some types of psychogenic analgesia may be mediated by emotional or cognitive (limbic or cortical)
drive of PAG
neurons. |
|
0 |
Devor; Pain Networks |
699 |
|
Stress-induced analgesia. |
|
0 |
Devor; Pain Networks |
699 |
|
Circuits
for the Modulation of
Pain Signals
(diagram) |
|
0 |
Devor; Pain Networks |
700 |
|
The reticular
core of the hindbrain is dominated by large neurons with widely
spread dendrites. |
|
1 |
Devor; Pain Networks |
700 |
|
Neurons in the isodendritic core of the brain stem receive defuse inputs from many sources and modalities, including nociceptive drive. |
|
0 |
Devor; Pain Networks |
700 |
|
Descending
inhibition can be turned on by sending noxious drive. |
|
0 |
Devor; Pain Networks |
700 |
|
Inflammation sensitizes nociceptive endings locally at sites of
trauma,
yielding primary hyperalgesia. |
|
0 |
Devor; Pain Networks |
700 |
|
Intractable pain states are associated with damage to peripheral nerves, spinal roots, or the CNS. |
|
0 |
Devor; Pain Networks |
700 |
|
The most dramatic example of neuropathic pain is phantom limb pain in amputees. |
|
0 |
Devor; Pain Networks |
700 |
|
Neuropathic pain includes various common
conditions such as diabetic neuropathy, low back pain with sciatica, and many instances
of cancer pain. |
|
0 |
Devor; Pain Networks |
700 |
|
Injury to peripheral nerves may also trigger
long-term changes in CNS somatosensory processing. |
|
0 |
Devor; Pain Networks |
701 |
|
In what
circuit does pain
sensation actually occur? The answer is not known. |
|
1 |
Devor; Pain Networks |
702 |
|
Quadriplegics
can experience pain. |
|
1 |
Devor; Pain Networks |
702 |
|
Extensive cortical
lesions do not
preclude pain sensation, even in parts
of the body whose cortical
representation has
been destroyed. |
|
0 |
Devor; Pain Networks |
702 |
|
Electrical stimulation of the cortex only rarely evokes pain. |
|
0 |
Devor; Pain Networks |
702 |
|
The most likely seat of pain sensation is in the brainstem and subcortical forebrain regions that are selectively activated by the noxious stimuli. |
|
0 |
Devor; Pain Networks |
702 |
|
Pain sensation may be related to the reticular
formation and its midbrain and thalamic extensions. |
|
0 |
Devor; Pain Networks |
702 |
|
Pain, particularly persistent
pain,
remains a medical health problem of the first order. |
|
0 |
Devor; Pain Networks |
702 |
|
Situated at the interface of body and
mind, the pain system is only a few synapses between the biophysics of stimulus
transduction
and the mysteries of perception, affect,
and belief. |
|
0 |
Tittle; Perception 3D Structure |
715 |
|
Perception of Three-Dimensional
Structure |
|
13 |
Tittle; Perception 3D Structure |
715 |
|
One of the most perplexing phenomena in the study
of human perception is the ability of observers to determine the layout and three-dimensional
(3D) structure of objects in the environment
from the two-dimensional
(2D) patterns of light that project
onto the retina. |
|
0 |
Tittle; Perception 3D Structure |
716 |
|
Review various functional modules that have been
proposed for analyzing
an object's 3D structure from optical information, such a shading, texture, motion,
and stereo. |
|
1 |
Tittle; Perception 3D Structure |
716 |
|
Structure from Shading |
|
0 |
Tittle; Perception 3D Structure |
716 |
|
Although there exist many
examples from both painting
and photography that image shading can be a perceptually salient source of
information about 3-D
structure, it is not at all obvious how the human visual system makes use of
this information. |
|
0 |
Tittle; Perception 3D Structure |
716 |
|
Structure from Surface Texture and Contour |
|
0 |
Tittle; Perception 3D Structure |
716 |
|
Using the relationship between texture size,
density, and 3D layout, the visual system could potentially
recover information about surface shape from the systematic variations in the projection of texture
elements. |
|
0 |
Tittle; Perception 3D Structure |
717 |
|
Structure from Motion |
|
1 |
Tittle; Perception 3D Structure |
717 |
|
When either the observer or an
object in the environment moves, the pattern of relative motion in the image
provides additional information about 3D structure. |
|
0 |
Tittle; Perception 3D Structure |
717 |
|
Structure from Binocular Disparity |
|
0 |
Tittle; Perception 3D Structure |
717 |
|
Binocular disparity (the angular difference between the images of corresponding features in the right and left eyes) has been considered one of the most powerful sources of 3D shape information. |
|
0 |
Tittle; Perception 3D Structure |
717 |
|
Structure from Multiple Sources |
|
0 |
Tittle; Perception 3D Structure |
718 |
|
Functional models for integrating multiple sources of information about 3D
structure include Bayesian
models, which can account for nonlinear (i.e. facilitatory) interaction. |
|
1 |
Tittle; Perception 3D Structure |
718 |
|
In visual
form perception, there may be aspects
of perceptual knowledge involved with motor
interactions (e.g. grasping
an object) that are not
directly accessible to our conscious
awareness. |
|
0 |
Tittle; Perception 3D Structure |
718 |
|
What exactly must be computed about an object's shape to successfully grasp it is a fundamental
question that is yet
to be answered. |
|
0 |
Zucker; Perceptual Grouping |
725 |
|
Perceptual Grouping |
|
7 |
Zucker; Perceptual Grouping |
725 |
|
While gazing
at the skies,
the ancients
saw more than just stars; they grouped
the stars into clusters and identified
the clusters
with objects
both real and mythological. |
|
0 |
Zucker; Perceptual Grouping |
725 |
|
Grouping functions to bind elements in a manner
such that Gestalt principles apply and the whole is
greater than the sum of its parts. |
|
0 |
Zucker; Perceptual Grouping |
725 |
|
Gestalt
proponents claimed that elements of a pattern are grouped together that are most proximal spatially; most proximal temporally; most similar geometrically; part of the most continuous pattern; part of the
most closed pattern; arranged in uniform density; evolving with common fate; most
symmetric;
exhibiting a common orientation; or optimizing an intuitive measure of "figural goodness" or pragnanz. |
|
0 |
Zucker; Perceptual Grouping |
727 |
|
Kanisza
(1975) subjective triangle illustrates the completion of a figure from separate,
localized figure elements. (diagram) |
|
2 |
Smith; Retina |
816 |
|
Retina |
|
89 |
Smith; Retina |
816 |
|
In the retina, a variety of
neuron types transform the visual signals in a multitude of ways to code
properties of the visual world, such as contrast and motion. |
|
0 |
Smith; Retina |
816 |
|
As a delegate visual signal is
amplified as it passes through the retina from photoreceptors to the ganglion
cells, the biological limitations of neural processing and distortion and
noise with every neuron. |
|
0 |
Smith; Retina |
816 |
|
Much of the retina signal coding
and structural detail is derived from the need to optimally amplify the
signal and eliminate noise. |
|
0 |
Smith; Retina |
816 |
|
Structure of the retina showing
the several layers and various cell types and connections. (diagram) |
|
0 |
Smith; Retina |
817 |
|
The retina comprises several
dozen cell types, defined by distinctive morphology, distribution, and
synaptic connection pattern, or a distinctive physiology or
immunocytochemical staining pattern. |
|
1 |
Smith; Retina |
817 |
|
The receptive
fields of retinal
neurons consist of a circular
region in visual
space to which the neuron
is most sensitive, called the center, and a larger, weaker antagonistic region
concentric with the center, called the surround. |
|
0 |
Smith; Retina |
817 |
|
There are
two classes of photoreceptors, rods and
cones. |
|
0 |
Smith; Retina |
817 |
|
Rods are sensitive to single photons and are bleached by daylight. |
|
0 |
Smith; Retina |
817 |
|
Cones are
approximately two log
units less sensitive
than rods and can regenerate their pigment in daylight. |
|
0 |
Smith; Retina |
818 |
|
At twilight, cones do not respond well, so rods are coupled through gap junctions to the neighboring cones, causing the rod
signal in twilight to pass directly into the cones, where it is carried
by the lower-gain cone pathway. |
|
1 |
Smith; Retina |
818 |
|
For the low
intensity range encountered at night, a special rod
bipolar pathway carries quantal single-photon signals, removes dark noise, and adapts over an extra three log units of intensity. |
|
0 |
Hepp; Saccades |
826 |
|
Saccades and Listing's Law |
|
8 |
Hepp; Saccades |
826 |
|
In humans and monkeys, the
saccadic system is the best studied and most fully understood voluntary motor
system. |
|
0 |
Hepp; Saccades |
826 |
|
With the head constrained
upright and fixed, and the eyes looking at distant targets, the saccade
system's function is to direct the fovea of the eye to the most interesting
part of the visual field. |
|
0 |
Hepp; Saccades |
826 |
|
The saccadic system is tightly
coupled to the visual system to replace by successive foveations the high
demands on neural circuitry, which a high-acuity panoramic vision would
otherwise require. |
|
0 |
Hepp; Saccades |
826 |
|
Saccades (French: brusque, irregular
movements) of the eye are a class of
rapid voluntary movements that we execute during alertness, one to three times per
second. |
|
0 |
Hepp; Saccades |
827 |
|
Saccade Generator in the Brainstem |
|
1 |
Arbib; Schema Theory |
830 |
|
Schema Theory |
|
3 |
Arbib; Schema Theory |
830 |
|
A schema is an active entity such as that
involving driving a car
or recognizing a tree. |
|
0 |
Arbib; Schema Theory |
830 |
|
When we learn a new task, we may quickly
approximate the skill by marshaling a stock of existing schemas and then tune the resulting assemblage through experience to emerge with a new schema for skilled performance of the task. |
|
0 |
Arbib; Schema Theory |
830 |
|
A schema is both a store
of knowledge
and
a description of a process for applying that knowledge. |
|
0 |
Stein; Scratch Reflex |
834 |
|
Scratch Reflex |
|
4 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
Selective Visual Attention |
|
3 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
In the human
visual system
the
amount of information
provided by the optic nerve -- estimated to be
in the range of 108-109 bits per second -- far exceeds what the brain is capable of fully processing and assimilating into conscious
experience. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
To deal with this information bottleneck, nature selects certain portions of the input to be processed preferentially, shifting the processing focus from one location to another. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
Selective visual attention is employed in a wide variety of biological vision systems, from jumping spiders to humans. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
A metaphor commonly used is that the visual
attention acts as a spotlight, enhancing information within a selected region of the image (or alternatively, filtering out information
outside of the spotlight). |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
837 |
|
Generally, events occurring within the focus of attention yield faster or more accurate detection
than events occurring outside the focus. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
There is evidence for at least
two different dynamical forms of attention -- one that is fast and transient,
and another that is slower and sustained. |
|
1 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The
transient component
of attention is driven involuntarily (i.e. by a moving object, or a flickering light). |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The sustained component of attention is under voluntary control (such as the way an athlete directs covert attention to track
other players). |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The speed with which the attentional
spotlight can move from one location to the next
appears to be on the order of 30-50 ms. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
Attention shifts 4 to 6 times faster than eye movements. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
A wide
variety of brain
areas is involved in visual
attention. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
Cortex, superior colliculus, and pulvinar all seem to be involved in various aspects of visual attention. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
Visual cortex
can be subdivided into two processing streams: an occipitotemporal stream that is mainly concerned with object
recognition,
and a occipitoparietal stream that is mainly concerned
with spatial relationships among objects and how one acts
on them through reaching and grasping. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The
superior colliculus is a structure in the midbrain that is involved in controlling eye movements. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The superior colliculus receives
as input from the retina and cortex and projects to the pulvinar (but has no
direct projection to the cortex). |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
838 |
|
The pulvinar is a large subcortical
structure, divided into at least four nuclei, that is part of the thalamus. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
839 |
|
To pulvinar is heavily interconnected with visual cortical areas. |
|
1 |
Olshausen
& Koch; Selective Visual Attention |
839 |
|
Humans would damage to specific
brain areas has shown a number of areas to be involved in the control of
attention. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
839 |
|
On the basis of research studies, it has been
proposed that the parietal cortex is responsible for disengaging
attention;
the superior colliculus, for moving attention; and the pulvinar, for engaging attention. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
839 |
|
The function of visual attention is to focus computational resources on a specific,
conspicuous or salient region within a scene. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
839 |
|
It has been proposed that the control structure underlying visual attention needs to represent locations within a topographic saliency map. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
840 |
|
The saliency
map may be represented in either the superior colliculus or parietal cortex. |
|
1 |
Olshausen
& Koch; Selective Visual Attention |
840 |
|
The saliency
map hypothesis predicts that receptive fields of cortical neurons should shift with attention, which is in
accordance with the observed dynamic changes in the receptive fields of V4 and IT neurons. |
|
0 |
Olshausen
& Koch; Selective Visual Attention |
840 |
|
Visual attention acts as a short-term binding mechanism for bringing together a select portion of visual information for higher-level analyses. |
|
0 |
von der
Malsburg; Self-Organization |
840 |
|
Self-Organization and the Brain |
|
0 |
von der
Malsburg; Self-Organization |
840 |
|
The process of network self-organization is fundamental to the organization of the brain. |
|
0 |
von der
Malsburg; Self-Organization |
841 |
|
Organization
takes place in systems consisting of a large
number of interacting
elements. |
|
1 |
von der
Malsburg; Self-Organization |
841 |
|
A fundamental and very important
characteristic about organizing systems is the global order that can arise from local interactions. |
|
0 |
von der
Malsburg; Self-Organization |
841 |
|
Many originally
random local fluctuations can coalesce into a globally ordered pattern of deviations from the original state. |
|
0 |
von der
Malsburg; Self-Organization |
841 |
|
The organization of a pattern is produced by the forces between elements and by initial and boundary conditions. |
|
0 |
von der
Malsburg; Self-Organization |
841 |
|
Neural interactions are not
necessarily topologically arranged; connected cells are "neighbors" although they may be located at different ends of the brain. |
|
0 |
von der
Malsburg; Self-Organization |
842 |
|
For Hebbian plasticity, the
requirements of self reinforcement and locality suffice to specify the
mechanism of synaptic plasticity in excitatory synapses. |
|
1 |
von der
Malsburg; Self-Organization |
842 |
|
The Hebbian plasticity rule for
a inhibitory synapses would have a synapse strength and if it is successful
in inhibiting the postsynaptic element. |
|
0 |
von der
Malsburg; Self-Organization |
842 |
|
The rules of cooperation and competition act on a local scale |
|
0 |
von der
Malsburg; Self-Organization |
842 |
|
The phenomenon of self-organization is the emergence of a globally ordered states. |
|
0 |
Taylor; Self-Organization in Time Domain |
843 |
|
Self-Organization in the Time
Domain |
|
1 |
Taylor; Self-Organization in Time Domain |
843 |
|
Time is a crucial aspect of incoming information. |
|
0 |
Taylor; Self-Organization in Time Domain |
843 |
|
There are many situations where
the ability to process, store, recognize, or recall temporal sequences of
patterns has great survival value to an animal. |
|
0 |
Taylor; Self-Organization in Time Domain |
843 |
|
For humans, language allows the efficient handling of high-level concepts. |
|
0 |
Taylor; Self-Organization in Time Domain |
843 |
|
Adaptive neural networks are
able to learn the temporal features on inputs. |
|
0 |
Taylor; Self-Organization in Time Domain |
843 |
|
To achieve learning of temporal
structures, there may be an ability, in either the network itself or in an
auxiliary structure, for the temporal character of the input to be buffered
temporarily. |
|
0 |
Ritter; Self-Organizing Maps |
846 |
|
Self-Organizing Feature Maps |
|
3 |
Ritter; Self-Organizing Maps |
846 |
|
A first and very important step
in many pattern recognition
and information processing tasks is the identification or construction of a reasonably small set of important features in which the essential information for the task is concentrated. |
|
0 |
Ritter; Self-Organizing Maps |
846 |
|
The self-organizing
feature map is an
approach by which features can be obtained by means of an unsupervised
learning process. |
|
0 |
Ritter; Self-Organizing Maps |
846 |
|
The feature map is a nonlinear method that is based on a layer of adaptive units, which gradually develop into an array of feature detectors that is
spatially organized
so that the location
of the excited units becomes
indicative of statistically important features
of the input signal. |
|
0 |
Barnden;
Semantic Networks |
854 |
|
Semantic Networks |
|
8 |
Barnden;
Semantic Networks |
856 |
|
One common and basic type of
processing in semantic networks is spreading activation. |
|
2 |
Barnden;
Semantic Networks |
856 |
|
During the course of spreading
activation, if one node is active in some moment, then the nodes directly
connected to it can become activated. |
|
0 |
Massone;
Sensorimotor Learning |
860 |
|
Sensorimotor Learning |
|
4 |
Massone;
Sensorimotor Learning |
860 |
|
Semantic networks are a way of
representing information abstractly. They are commonly used in symbolic
cognitive science. |
|
0 |
Guigon; Short-Term Memory |
867 |
|
Short-Term Memory |
|
7 |
Guigon; Short-Term Memory |
868 |
|
Short-term memory may also be a property of large-scale
neuronal architecture involving cortical and subcortical regions of the brain. |
|
1 |
Guigon; Short-Term Memory |
869 |
|
Memory processes in the hippocampus appear to be based on three
different forms of plasticity within a serially
organized anatomical circuit that comprises the corticohippocampal pathways
(entorhinal cortex --> dentate --> CA3 --> Ca1). |
|
1 |
Guigon; Short-Term Memory |
869 |
|
Synaptic potentiation can persist for hours in mossy fibers (between dentate and CA3), for several
days in cortical
projections to the dentate gyrus, for several
weeks in CA1. |
|
0 |
Favorov;
Somatosensory System |
884 |
|
Somatosensory System |
|
15 |
Favorov;
Somatosensory System |
884 |
|
Primary somatosensory cortex
(SI),
consisting of cytoarchitectural areas 3a, 3b, 1, and 2. |
|
0 |
Favorov;
Somatosensory System |
885 |
|
Studies of rodent
whisker barrels have addressed the question of how the barrels of rodent
SI are formed. |
|
1 |
Favorov; Somatosensory System |
885 |
|
Studies have found that the barrels of rodent whisker cortical fields
closely approximate Dirichlet domains (which subdivide a field into convex polygons, each having a center point and consisting of all points that are closer to the center
point than to those of other
polygons). |
|
0 |
Favorov;
Somatosensory System |
885 |
|
The thalamocortical
fibers that carry
information from any
particular facial whisker initially terminate in the SI in an approximately
Gaussian distribution, partially overlapping the projections from neighboring whiskers. |
|
0 |
Favorov;
Somatosensory System |
885 |
|
The regions from a Dirichlet-like pattern in the SI, with the center point of each Dirichlet domain coinciding
with the peak of the Gaussian distribution of a particular whisker. |
|
0 |
Favorov;
Somatosensory System |
885 |
|
Within each Dirichlet
domain, the coherently
acting thalamocortical fibers of the dominant whisker-based group cooperate with each other and compete with fibers from other whisker-based groups. |
|
0 |
Favorov;
Somatosensory System |
885 |
|
The fibers of the dominant group arborize extensively within their
domain,
while those from other groups withdraw from
it, thus
producing the typical discrete clustering of afferents characteristic of the barrel cortex. |
|
0 |
Favorov; Somatosensory System |
886 |
|
To achieve the local receptive field variations characteristic of living sensory
networks and to preserve topological orderliness on a more global scale, a connection
pattern exists in which near neighbors have inhibitory lateral
interactions and more distant neighbors have excitatory interactions. |
|
1 |
Favorov;
Somatosensory System |
886 |
|
The living
cortical network has a pattern
of lateral
connectivity provided by the axons of inhibitory double-bouquet cells and excitatory spiny stellate cells. |
|
0 |
Favorov;
Somatosensory System |
886 |
|
Connections
from the thalamus to minicolumns in the cortex were plastic; they were allowed to self-organize in accordance with a
Hebbian rule during a
developmental period. |
|
0 |
Favorov;
Somatosensory System |
886 |
|
Through self-organization, a segregate acquires a complex,
richly detailed pattern
of thalamic connections
in which neighboring minicolumns choose connections from partially shifted groups of thalamic neurons, reproducing experimentally
observed features of the termination patterns of thalamocortical axons. |
|
0 |
Foldiak; Sparse Coding |
895 |
|
Sparse Coding in Primate Cortex |
|
9 |
Foldiak; Sparse Coding |
895 |
|
A general goal in the study of cortical function is to understand
how states of the environment are represented by firing patterns of cortical neurons. |
|
0 |
Foldiak;
Sparse Coding |
895 |
|
Is sensory
information represented by the activity of single, individually meaningful cells, or is it only the global
activity pattern across a whole cell population that
corresponds to interpretable states? There are strong theoretical reasons and
experimental evidence suggesting that the brain adopts a compromise between these extremes,
which is often referred to as sparse coding. |
|
0 |
Foldiak; Sparse Coding |
896 |
|
Distributed representations are tolerant
to damage. |
|
1 |
Foldiak;
Sparse Coding |
896 |
|
Redundancy
far smaller than that in dense codes is sufficient to produce robust behavior. By simply duplicating units with 99%
reliability (assuming independent failures), reliability increases to 99.99%. |
|
0 |
Foldiak; Sparse Coding |
896 |
|
Alzheimer's patients may irreversibly lose specificity, and even whole concepts, independently for individual objects. |
|
0 |
Foldiak; Sparse Coding |
897 |
|
At any moment
during an animal's life, only a small fraction of neurons will be strongly activated by natural stimuli. |
|
1 |
Foldiak; Sparse Coding |
897 |
|
In inferotemporal
cortex (IT),
cells shows selectivity for complex visual patterns and objects, such as faces, hands, complex geometrical shapes, and fractal patterns, and the responses are usually not
predictable from responses to simple stimuli. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
Thalamocortical Oscillations in
Sleep and Wakefulness |
|
79 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
The brain spontaneously
generates
complex patterns of neural
activity. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
As the brain enters slow-wave sleep, the rapid patterns characteristic of
the aroused state are replaced by low-frequency, synchronized rhythms of neuronal
activity. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
Entering slow-wave
sleep, EEG recordings shift from low-amplitude, high
frequency rhythms to large-amplitude,
slow oscillations. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
Studies of forebrain
responsiveness and specific cellular mechanisms suggest that sleep oscillations are highly orchestrated and highly regulated. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
Experimental and modeling
studies have shown that sleep rhythms emerge from an interaction between the intrinsic properties of
neurons and the networks through which they interact. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
976 |
|
The thalamus and cerebral cortex are intimately linked by reciprocal projections. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
977 |
|
A delta
frequency rhythm (1-4 Hz) can be generated in single cells by the interplay of two intrinsic currents of thalamocortical neurons. |
|
1 |
Sejnowski;
Thalamocortical Oscillations |
977 |
|
Hyperpolarization of thalamocortical cells is a critical factor for the interplay between currents that generate delta oscillation. A hyperpolarization of 10 mV can lead to spontaneous, self-sustained delta oscillations. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
978 |
|
Spindle
oscillations consists of 7-14 Hz waxing and waning field potentials, grouped in
sequences that last for 1-3 seconds and recur
every 3-10 seconds. |
|
1 |
Sejnowski;
Thalamocortical Oscillations |
979 |
|
The spindles of natural sleep
are related to the development of a peculiar pattern of oscillatory activity,
the spike-and-wave EEG complexes, which are associated with absence (petite
mal) epileptic seizures. |
|
1 |
Sejnowski;
Thalamocortical Oscillations |
979 |
|
In arousal, electrical
activation of certain brainstem and hypothalamic regions, including the
reticular activating system, causes a variety of neurotransmitters, including
acetylcholine, norepinephrine, serotonin (5-HT), histamine, and glutamate to
be released through diffuse ascending axonal arborizations. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
979 |
|
On arousal, the low-frequency oscillations of the
cortical EEG
disappear and are replaced by higher frequency (20-80 Hz, mainly
around 40 Hz)
rhythms. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
979 |
|
Neurons throughout the nervous
system (e.g. the retina, collateral genetic let nucleus, and cortex) have the
ability to generate repetitive trains of action potentials in the frequency
range of 20-80 Hz, although the synchronization of this activity and behaviorally
irrelevant subgroups of widely spaced neurons has only been demonstrated in
the cerebral cortex. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
The diversity of cortical cells and their complex interactions make it difficult to model cortical networks with the same confidence with which thalamic networks have been modeled. |
|
1 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Models of cortical networks reveal the need to regulate the tendency of recurrent networks to oscillate. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
The excitability of neurons can be controlled by inhibition. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Inhibition
is an efficient mechanism for synchronizing large populations of pyramidal neurons because of voltage-dependent
mechanisms in their somas and the strategic location of inhibitory boutons on the initial segments of axons, where action potentials are initiated. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Realistic simulations of cortical neurons showed that sparse excitatory connectivity
between distant
populations of neurons can produce synchronization within one or two
cycles,
but only if the long-range connections are made on inhibitory as well as excitatory neurons. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Dreams
occurred during REM sleep, characterized by an abolition of low frequency oscillations and an increase in cellular excitability, much like wakefulness, although motor output is significantly inhibited. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
There is no
generally accepted function for dreams or for the sleep state itself. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
During spindling and slow wave sleep, the thalamus excites the cortex with patterns of
activity that are more spatially and temporally coherent
than normally would be encountered in the awake
state. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
The polarizing pulses of calcium
that enter the thalamic and cortical neurons may influence enzyme case
cascades that regulate gene expression, only a statically adjusting the
balance of ionic currents and regulatory mechanisms. This widespread activity
could be used to reorganize cortical networks after learning occurs during
the awake state. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Inhibitory neurons in the thalamus and cortex are of particular importance in producing the synchrony and controlling the spatial extent of coherent populations. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Synchrony and other network
properties could be used to control the flow of information between brain
areas and to decide where to store important information. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
Synchronization enhances the strength of signals, but also reduces the amount of information that can be encoded. |
|
0 |
Sejnowski;
Thalamocortical Oscillations |
980 |
|
The ascending neuromodulatory
transmitter systems delicately tuned the state and excitability of the
different parts of the nervous system so that it is appropriate for the
analysis of sensory information, the cognitive processing and storage of this
information, and the subsequent performance of the appropriate neuronal and
behavioral responses. |
|
0 |
Mumford; Thalamus |
981 |
|
Thalamus |
|
1 |
Mumford; Thalamus |
981 |
|
The thalamus is a subdivision of the brain of all
mammals. |
|
0 |
Mumford; Thalamus |
981 |
|
The thalamus is shaped roughly
like a pair of small eggs, with cell count of approximately 2% -- 4% of the
cortex. |
|
0 |
Mumford; Thalamus |
981 |
|
Essentially all
input to the cortex is relayed through the thalamus. |
|
0 |
Mumford; Thalamus |
981 |
|
Visual, auditory, and
somatosensory information is input to the cortex via the thalamus |
|
0 |
Mumford; Thalamus |
981 |
|
Planning-tuning-motor output of
the basal ganglia and cerebellum are input to the cortex via the thalamus. |
|
0 |
Mumford; Thalamus |
981 |
|
Emotional-motivational output of
the mammillary body reaches the cortex through the thalamus. |
|
0 |
Mumford; Thalamus |
981 |
|
The thalamus is the principal
gateway to the cortex |
|
0 |
Mumford;
Thalamus |
981 |
|
The thalamocortical
pathways are reciprocated by feedback pathways from the cortex back to the thalamus, forming a massive system of local loops between a thalamus and the entire cortex. |
|
0 |
Mumford; Thalamus |
981 |
|
There is approximately 10 times
more feedback from the cortex to the thalamus then feedforward paths. |
|
0 |
Mumford; Thalamus |
981 |
|
Simplicity
of the internal circuitry of the thalamus. |
|
0 |
Mumford; Thalamus |
981 |
|
Most of the cells of the thalamus are excited by subcortical input and send the output directed to the cortex, with no collaterals to other thalamic cells. |
|
0 |
Mumford; Thalamus |
981 |
|
The thalamus is not a homogeneous mass of neurons, but a collection of smaller nuclei. |
|
0 |
Mumford; Thalamus |
981 |
|
In humans, there are approximately 50 thalamic nuclei in each hemisphere. |
|
0 |
Mumford; Thalamus |
981 |
|
Most of the
thalamic nuclei are specific nuclei that connect in an orderly topological pattern one nucleus to one area of the cortex. |
|
0 |
Mumford;
Thalamus |
981 |
|
The projection of the LGN in the thalamus to cortical area V1 preserves the separation of the
signals from the two
eyes onto distinct ocular dominance columns in area V1. |
|
0 |
Mumford; Thalamus |
981 |
|
In addition to specific nuclei, there are also nonspecific
nuclei in the thalamus that project diffusely, often onto the entire cortex. |
|
0 |
Mumford; Thalamus |
981 |
|
The principal
cell type in the specific
nuclei of the thalamus are excitatory cells known as "relay
cells." |
|
0 |
Mumford; Thalamus |
981 |
|
The axons of relay cells go directly
to the cortex, giving
off no collaterals, except on cells in the reticular
nucleus as they pass
through. |
|
0 |
Mumford; Thalamus |
981 |
|
The cortical relay cell axons synapse principally in the cortex layer 4 or deep layer 3, the standard input layers of the cortex. |
|
0 |
Mumford; Thalamus |
981 |
|
In drowsiness or non-REM sleep, the thalamic relay cells go into an oscillatory mode in which they alternate between short, high-frequency bursts
and
extended periods of hyperpolarization, repeating at a frequency of 7-14 Hz. |
|
0 |
Mumford; Thalamus |
982 |
|
Active Roles for the Thalamic
Buffer |
|
1 |
Mumford; Thalamus |
982 |
|
A model is proposed in which the feedback
pathways serve to modify
afferent sensory stimuli in ways that enhance and complete sensory input
patterns,
suppress irrelevant features, and generate quasi-sensory patterns when afferent stimulation is weak or absent. |
|
0 |
Mumford; Thalamus |
982 |
|
Optimizing by "stimulated
annealing" an objective function which seeks to enhance remembered
patterns partially or noisily present in the input. |
|
0 |
Mumford; Thalamus |
982 |
|
The idea that an iterative algorithm is carried out
in the thalamocortical
loop has received experimental
confirmation in observed
oscillations. |
|
0 |
Mumford; Thalamus |
983 |
|
The concept of generating completed sensory
patterns from memory when the actual stimulus is noisy
or incomplete. |
|
1 |
Mumford;
Thalamus |
983 |
|
Feedback
may represent a process of actively creating from memory synthetic
patterns that try to match
as closely as possible the current stimulus. |
|
0 |
Mumford;
Thalamus |
983 |
|
The feedback signal is the
pattern synthesized from memory, and the feedforward signal contains features
of the stimulus or of the difference between the stimulus and the feedback
(the residual). |
|
0 |
Mumford;
Thalamus |
983 |
|
Bayesian statistical procedures have been applied to the ideas of actively
creating from memory synthetic patterns that try to match as closely as possible the current stimulus. |
|
0 |
Mumford; Thalamus |
983 |
|
In one proposal, the area V1 to
LGN pathway carries negative feedback so that after iteration, area V1
converges to the pattern of activity whose feedback closely matches the most
salient visual patterns in the stimulus. This type of algorithm is called "feedback-pattern-synthesis." |
|
0 |
Mumford;
Thalamus |
983 |
|
Top-down
data may be used to enhance the bottom-up signals, to reconstruct
missing data, or to externalize for further processing, views of the world created purely
by mental imagery. |
|
0 |
Mumford;
Thalamus |
983 |
|
The retinal signals are noisy
and complex, with multiple physical effects creating a highly coded, but
incomplete view of three-dimensional objects and their illumination. |
|
0 |
Mumford; Thalamus |
983 |
|
The cortex must disentangle the effects of lighting, texture, shape,
and depth. |
|
0 |
Mumford;
Thalamus |
983 |
|
The hypothesis is that small numbers of buffers in the thalamic nuclei are used to combine the reconstructions made by various cortical specialty areas. |
|
0 |
Mumford; Thalamus |
983 |
|
Many
cortical specialty areas can search
independently for a large variety of patterns in the image, sending them all to the thalamus, where a kind of voting takes place by summation in the dendritic arbors of the relay cells and by inhibition through the interneurons. |
|
0 |
Mumford; Thalamus |
983 |
|
The active
thalamic nuclei can
be used to decide which
representation is most successful, rejecting the weaker matches |
|
0 |
Mumford; Thalamus |
983 |
|
As a result of corticothalamic iteration, the thalamic pattern of activity is sent back to the cortex as an enhanced view of the world. |
|
0 |
Poppel; Time Perception |
987 |
|
Time Perception: Problems of
Representation and Processing |
|
4 |
Poppel; Time Perception |
987 |
|
Time processing in the brain must be conceived of as being reconstructed on the basis of
events perceived |
|
0 |
Poppel; Time Perception |
987 |
|
Anatomical studies indicate the
segregation of elementary functions and their modular representation in the
brain. |
|
0 |
Poppel; Time Perception |
987 |
|
Each mental act is characterized by simultaneous neuronal activity in different brain areas. |
|
0 |
Poppel; Time Perception |
987 |
|
Spontaneous speech -- to speak
a sentence requires that lexical,
syntactical, sematic, phonetic, and prosodic competencies are all
operative. |
|
0 |
Poppel; Time Perception |
987 |
|
In the brain's
functionality comprising time perception, a number of elementary temporal experiences can be distinguished. |
|
0 |
Poppel; Time Perception |
987 |
|
The most
basic phenomenon in time
perception is the experience of simultaneity versus non-simultaneity of stimuli. |
|
0 |
Poppel; Time Perception |
987 |
|
Non-simultaneity is necessary for the
experience of temporal order or successiveness of stimuli. |
|
0 |
Poppel; Time Perception |
987 |
|
If different
stimuli can be related
to each other with respect to the before-after relationship, they are defined as representing discrete primoridal events. |
|
0 |
Poppel; Time Perception |
987 |
|
Successive events are integrated up to approximately 2 to 3 seconds to set up an operational temporal window. |
|
0 |
Poppel; Time Perception |
987 |
|
The temporal
window can be referred to as a subjective present. |
|
0 |
Poppel; Time Perception |
987 |
|
The elementary
temporal experience of duration is based on the amount
of information processed or events perceived within basic
integration units of a
few seconds. |
|
0 |
Poppel; Time Perception |
987 |
|
Subjective time within this hierarchical taxonomy of elementary temporal
experiences
is
analyzed by looking at and looking for neuronal
mechanisms of information
processing. |
|
0 |
Poppel; Time Perception |
988 |
|
Simultaneity, Nonsimultaneity,
and Succession |
|
1 |
Poppel; Time Perception |
988 |
|
To be perceived
as non-simultaneous, two discrete physical stimuli must
be separated by a minimal time interval, the fusion threshold, which is different for different sensory systems. |
|
0 |
Poppel; Time Perception |
988 |
|
Two auditory stimuli have to be separated by 1 to 2 ms to be heard is non-simultaneous, but visual or tactile stimuli separated by such intervals are still perceived as simultaneous. |
|
0 |
Poppel; Time Perception |
988 |
|
The auditory
system has the best, visual system the worst temporal acuity. |
|
0 |
Poppel; Time Perception |
988 |
|
Stimuli
must be separated in
the 40 Hz domain when the order has to be indicated. |
|
0 |
Poppel; Time Perception |
988 |
|
The order threshold seems to be the same for different sensory mode modalities (vision,
hearing, touch). |
|
0 |
Poppel; Time Perception |
988 |
|
If two
stimuli can be put into temporal order, it is necessary
that they be defined as independent entities, which are called primordial events. The primordial
events provide the raw material for further cognitive processing. |
|
0 |
Poppel; Time Perception |
988 |
|
Oscillations
as an Organizing Principle |
|
0 |
Poppel; Time Perception |
988 |
|
Reaction times between multimodal sensory input show a 30-40 ms temporal interval between
adjacent modes. |
|
0 |
Poppel; Time Perception |
988 |
|
Sequencing of Events |
|
0 |
Poppel; Time Perception |
989 |
|
Automatic Temporal Binding: The Subjective Present |
|
1 |
Poppel; Time Perception |
989 |
|
Duration Estimation |
|
0 |
Lund; Visual Cortex Cell Types |
1016 |
|
Visual Cortex Cell Types and
Connections |
|
27 |
Lund; Visual Cortex Cell Types |
1016 |
|
The anatomy of the visual system has been studied extensively in the macaque monkey, whose visual system is believed to be very similar to that of humans. |
|
0 |
Lund; Visual Cortex Cell Types |
1016 |
|
Two main groups of neurons are present within the cortex; the most numerous
group (approximately 80%) are excitatory in their influence on other cells and are characterized morphologically by having small spines on their dendritic surfaces that act as specialized sites for synaptic
contact. |
|
0 |
Lund; Visual Cortex Cell Types |
1016 |
|
Spiny neurons
either have dendritic processes of equal length -- and are called stellate neurons -- or have one
dendrite on the pial aspect of the cell that is greatly extended compared to
others -- and are call pyramidal neurons. |
|
0 |
Lund; Visual Cortex Cell Types |
1016 |
|
The less numerous (20%) group of cortical neurons,
the interneurons, are
generally stellate in
morphology, but
largely lack dendritic spines, contain GABA as their synaptic transmitter and are inhibitory in their output to other cells. |
|
0 |
Nelson; Visual Scene Perception |
1024 |
|
Visual Scene Perception:
Neurophysiology |
|
8 |
Nelson; Visual Scene Perception |
1024 |
|
Parietal
cortical areas are concerned with perceiving and learning the spatial arrangement of objects, while object
recognition depends heavily on the temporal areas. |
|
0 |
Nelson; Visual Scene Perception |
1025 |
|
Wiring diagram for the primate
visual system (diagram) |
|
1 |
|
|
|
|
|
|
|
|
|
|
|
|