Arbib - Handbook of Brain Theory and Neural Networks
Book	Page		Topic
Abbott; Activity Neuronal	63		Activity Dependent Regulation of Neuronal Conductances
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.		0
Carpenter; Adaptive Resonance (ART)	79		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		Associative Networks		11
Anderson; Associative Networks	102		The operation of association involves the linkage of information with other information.		0
Anderson; Associative Networks	102		Association is the most natural form of neural network computation.		0
Anderson; Associative Networks	103		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 associaters, 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		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		The ability of autoassociative systems to reconstruct missing or noisy parts of the learned patterns.		0
Mountains; Auditory Periphery	115		Auditory Periphery and Cochlear Nucleus		10
Mountains; Auditory Periphery	115		The auditory periphery transforms a very high information rates signal into a group of lower information rate signals.		0
Mountains; 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 per second.		0
Mountains; 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
Mountains; 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
Mountains; 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
Mountains; Auditory Periphery	115		The major output of the second stage of auditory processing consists of the receptor potentials of the inner hair cells.		0
Mountains; 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
Mountains; 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
Mountains; 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 topological a mapped onto the striatum,    which represent the "input" portion of the basal ganglia.		0
Alexander; Basal Ganglia	140		The cortically directed signal that emerged 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 the overlapping of 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 shot. After repeated pairings of the CS followed by the US, animals eventually learn to bleak in response to the CS, and this is called a conditioned response (CR).		0
Bartha; Cerebellum and Conditioning	169		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 do 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 a 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			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.		-360
Beeman; Emotion-Cognition			The mere exposures in effect precludes the involvement of conscious cognition, but not of unconscious cognition.		0
Beeman; Emotion-Cognition			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			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.		360
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 the motion 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 lobo 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 a coated in terms of its goal ("grass," "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 in motor neurons controlling individual muscles while 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 or process 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 a 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 my synapse-by-synapse bases independent of local activity.		0
Brown; Hebbian Plasticity	455		Whether a change occurs and he had been 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 and a handy and 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 regions 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 last 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 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 costimulation 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 to 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 grey 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 in activation) 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 the resume 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 debt for 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 the main, 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. pitch, hot, cold) or are polymodal.		0
Devor; Pain Networks	698		All nociceptors have slowly conducting axons within on no myelin.		0
Devor; Pain Networks	698		The firing frequency of each primary afferent type encodes only a narrowr 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 a 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 a widely spread dendrites.		1
Devor; Pain Networks	700		Neurons in the iso-dendritic 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 phenomenon 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 group 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 singles-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		Saccade's (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 entities 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 skill performance of a 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 select 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 on 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 she has 4 to 6 times faster than on movements.		0
Olshausen & Koch; Selective Visual Attention	838		A wide variety of brain areas are involved in visual attention.		0
Olshausen & Koch; Selective Visual Attention	838		Cortex, superior colliculus, and pull them 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 has a structure in the mid brain 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 that is part of the thalamus and is itself divided into at least four nuclei.		0
Olshausen & Koch; Selective Visual Attention	838		To pulvinar is heavily interconnected with visual cortical areas.		0
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.		1
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 all parietal cortex.		1
Olshausen & Koch; Selective Visual Attention	840		The saliency map 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 topological in arranged; connected selves 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, all recall temporal sequences of patterns is of 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 and 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	869		Memory processes in the hippocampus appeared 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).		2
Guigon; Short-Term Memory	869		Synaptic potentiation can persist for hours in mousey 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 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		A model of the SI segregate was developed with many columns built around a connectional pattern.		0
Favorov; Somatosensory System	886		Connections from the thalamus to minicolumns 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, is 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 end in dance 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 all 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 brains functionality comprising time perception, a number of elementary Rule 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 success in this stimuli.		0
Poppel; Time Perception	987		If different stimuli can be related to each other with respect to the book for-actress relationship, they are defined as representing discrete prime Oracle 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 process or events perceived within basic integration units of a few seconds.		0
Poppel; Time Perception	987		Subjected 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 LOCAL stimuli separated by such intervals are still perceived as simultaneous.		0
Poppel; Time Perception	988		The auditory system as the best, visual system the worst temporal acuity.		0
Poppel; Time Perception	988		Stimuli must be separated in the 40 Hz domain when they order has to be indicated.		0
Poppel; Time Perception	988		The order threshold seems to be the same for different sensory mode allergies (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