Rolls; Memory, Attention, and Decision-Making
Book	Page		Topic
Rolls; Memory, Attention, and Decision-Making	1		To gain an understanding of neural processes and consciousness it is helpful to simulate the brain's processing on a computer, and to show whether the simulation can perform the task of memory systems in the brain, and whether the simulation has similar properties to the real brain.
Rolls; Memory, Attention, and Decision-Making	1		Attention and decision-making can be understood in terms of interactions between the fundamental operations in memory systems in the brain.		0
Rolls; Memory, Attention, and Decision-Making	1		Empirical evidence to validate the understanding of neural processes and consciousness is largely from nonhuman primates and from humans, because of the considerable similarity of their memory and related systems.		0
Rolls; Memory, Attention, and Decision-Making	3		Many of the processes involved in memory, perception, attention, and decision-making in the brain can be understood in terms of the operation of different types of memory networks and the interactions between these networks.		2
Rolls; Memory, Attention, and Decision-Making	3		To understand memory, perception, attention, and decision-making in the brain, we are dealing with large-scale computational systems with interactions between the parts.		0
Rolls; Memory, Attention, and Decision-Making	3		Understanding cognitive functions such as object recognition, memory recall, attention, and decision-making requires single neuron data to be closely linked to the computational models of how the interactions between large numbers of neurons in many networks of neurons allow these cognitive problems to be solved.		0
Rolls; Memory, Attention, and Decision-Making	3		Information is exchanged by the spiking activity between the neurons of the brain.		0
Rolls; Memory, Attention, and Decision-Making	3		It is important to analyze the activity of single neurons and populations of neurons in order to understand brain function.		0
Rolls; Memory, Attention, and Decision-Making	3		Understanding how neurons represent information is fundamental for understanding how neurons and networks of neurons read the code from other neurons, and the actual nature of the computation that could be performed in a memory network.		0
Rolls; Memory, Attention, and Decision-Making	3		Neurocomputational models enable the single neuron level of analysis to be linked to the level of large-scale neuronal networks and the interactions between them, so that large-scale processes such as memory retrieval, object recognition, attention, and decision-making can be understood.		0
Rolls; Memory, Attention, and Decision-Making	8		Long-term potentiation and long term depression as models of synaptic modification.		5
Rolls; Memory, Attention, and Decision-Making	9		Schematic illustration of synaptic modification rules as revealed by long-term potentiation (LTP) and long term depression (LTD). (diagram)		1
Rolls; Memory, Attention, and Decision-Making	11		In that LTP is long-lasting, develops rapidly, is synapse specific, and is in some cases associative, it is of interest as a potential synaptic mechanism underlying some forms of memory.		2
Rolls; Memory, Attention, and Decision-Making	13		A sparse distributed representation is a distributed representation in which a small portion of the neurons is active at any one time.		2
Rolls; Memory, Attention, and Decision-Making	14		One advantage of distributed encoding is that the similarity between two representations can be reflected by the correlation between the two patterns of activity that represent the different stimuli.		1
Rolls; Memory, Attention, and Decision-Making	14		Another advantage a distributed encoding is that the number of different stimuli that can be represented can be very large.		0
Rolls; Memory, Attention, and Decision-Making	18		Pathways involved in some different memory systems (diagram)		4
Rolls; Memory, Attention, and Decision-Making	22		Fine Structure and Connectivity of the Neocortex		4
Rolls; Memory, Attention, and Decision-Making	22		The neocortex consists of many areas that can be distinguished by the appearance of the cells (cytoarchitecture) and fibers or axons (myeloarchitecture), but nevertheless, the basic organization of the different neocortical areas has many similarities.		0
Rolls; Memory, Attention, and Decision-Making	22		Excitatory Cells and Connections		0
Rolls; Memory, Attention, and Decision-Making	23		The main information bearing afferents to a cortical area have many terminals in layer 4.		1
Rolls; Memory, Attention, and Decision-Making	23		Primary sensory cortical areas received their inputs from the primary sensory thalamic nucleus associated with the specific sensory modality.		0
Rolls; Memory, Attention, and Decision-Making	23		Spiny stellate cells are so-called because they have radially arranged, star-like dendrites.		0
Rolls; Memory, Attention, and Decision-Making	23		Spiny stellate cell axons usually terminate within the cortical area in which they are located.		0
Rolls; Memory, Attention, and Decision-Making	23		Each thalami axon makes 1000 -- 10,000 synapses, not more than several (or at most 10) of which are onto any one spiny stellate cell.		0
Rolls; Memory, Attention, and Decision-Making	23		In non-primary sensory areas, important information bearing afferents from a preceding cortical area terminate in layer 4, but they are no or few spiny stellate cells in this layer.		0
Rolls; Memory, Attention, and Decision-Making	23		Layer 4 still looks granular (due to the presence of many small cells), but these cells are typically small pyramidal cells.		0
Rolls; Memory, Attention, and Decision-Making	23		Spiny stellate cells and small pyramidal cells are similar in many ways.		0
Rolls; Memory, Attention, and Decision-Making	23		The axons of the superficial (layer 2 and 3) pyramidal cells have collaterals and terminals in layer 5.		0
Rolls; Memory, Attention, and Decision-Making	23		The axons of the superficial (layer 2 and 3) pyramidal cells axons also typically project out of that cortical area, and onto the next cortical area in sequence, where they terminate in layer 4, forming the forward cortico-cortical projection.		0
Rolls; Memory, Attention, and Decision-Making	23		It is also from the superficial layer 2 and 3 pyramidal cells that projections to the amygdala arise in some sensory areas that are high in the hierarchy.		0
Rolls; Memory, Attention, and Decision-Making	23		Axons of layer 5 pyramidal cells have many collaterals in layer 6. Axons of these layer 6 cells typically leave the cortex to project to subcortical sites (such as the striatum), or back to the preceding cortical area to terminate in layer 1.		0
Rolls; Memory, Attention, and Decision-Making	23		There are as many of these back projections as there are forward connections between two sequential cortical areas.		0
Rolls; Memory, Attention, and Decision-Making	24		Layer 6 pyramidal cells have prolific dendritic arborizations in layer 4 and receive synapses from thalami afferents and also from pyramidal cells in other cortical layers.		1
Rolls; Memory, Attention, and Decision-Making	24		Layer 6 pyramidal cells form backprojections to the thalmic nucleus from which it receives projections, .		0
Rolls; Memory, Attention, and Decision-Making	24		Axons of layer 6 cells also contribute to the backprojections to layer 1 of the preceding cortical area.		0
Rolls; Memory, Attention, and Decision-Making	24		Although the pyramidal and spiny stellate cells form the great majority of neocortical neurons with excitatory outputs, there are in addition several further cell types.		0
Rolls; Memory, Attention, and Decision-Making	24		Bipolar cells are found in layers 3 and 5, and are characterized by having two dendritic systems, one ascending and the other descending, which together with the axon distribution, are confined to a narrow vertical column often less than 50 µ in diameter.		0
Rolls; Memory, Attention, and Decision-Making	24		Inhibitory Cells and Connections		0
Rolls; Memory, Attention, and Decision-Making	24		There are a number of types of neocortical inhibitory neurons, all having no spines and using GABA as a neurotransmitter.		0
Rolls; Memory, Attention, and Decision-Making	24		A number of types of inhibitory neurons can be distinguished, best by their axonal distributions.		0
Rolls; Memory, Attention, and Decision-Making	24		One type of inhibitory neuron, the basket cell, present in layers 3--6, has few spines on his dendrites so that it can be described as smooth, and has an axon that participates in the formation of weaves of preterminal axons which surround the cell bodies of pyramidal cells, and form synapses directly on the cell body, but also onto the dendritic spines.		0
Rolls; Memory, Attention, and Decision-Making	24		Basket cells comprise 5 -- 7% of the total cortical cell population, compared with approximately 72% for pyramidal cells.		0
Rolls; Memory, Attention, and Decision-Making	24		Basket cells receive synapses from the main extrinsic afferents to the neocortex, including thalami afferents, so they must contribute to a feedforward type of inhibition of pyramidal cells.		0
Rolls; Memory, Attention, and Decision-Making	24		They feedforward mechanism of basket cells allows the pyramidal cells to be set to an appropriate sensitivity for the input they are about to receive.		0
Rolls; Memory, Attention, and Decision-Making	24		The neurotransmitter used by the basket cells is GABA.		0
Rolls; Memory, Attention, and Decision-Making	25		Dendrites of basket cells can extend laterally 0.5 mm or more (primarily within the layer at which the cell body is located), and the axons can also extend laterally from the cell body 0.5 -- 1.5 mm.		1
Rolls; Memory, Attention, and Decision-Making	25		The basket cells produce a form of lateral inhibition that is quite spatially extensive.		0
Rolls; Memory, Attention, and Decision-Making	25		Each basket cell may make 4--5 synapses with a given pyramidal cell;    each pyramidal cell may receive from 10--30 basket cells;    and each basket cell may inhibit approximately 300 pyramidal cells.		0
Rolls; Memory, Attention, and Decision-Making	26		The number of synapses per neuron may be 40,000 for humans.		1
Rolls; Memory, Attention, and Decision-Making	26		The great majority of cortical excitatory synapses are made from axons of cortical pyramidal cells.		0
Rolls; Memory, Attention, and Decision-Making	26		Micro-anatomical studies show that pyramidal cells rarely make more than one connection with any other pyramidal cell, even when they are adjacent in the same area of the cerebral cortex.		0
Rolls; Memory, Attention, and Decision-Making	26		The probability that a pyramidal cell makes a synapse with its neighbor is low, approximately 0.1.		0
Rolls; Memory, Attention, and Decision-Making	26		Within a cortical area of perhaps 1 mm², the region within which typical pyramidal cells have dendritic trees and their local axonal arborization, there is a probability of excitatory-to-excitatory cell connection of 0.1.		0
Rolls; Memory, Attention, and Decision-Making	26		The local population of mutually interconnected pyramidal neurons is served by its own population of inhibitory neurons, which have a spatial receiving and sending zone and the order of 1 mm2.		0
Rolls; Memory, Attention, and Decision-Making	26		The architecture of local pyramidal neurons and inhibitory neurons is the effectively recurrent or reentrant. It may be expected to show some of the properties of recurrent networks, including fast dynamics.		0
Rolls; Memory, Attention, and Decision-Making	26		The fast dynamics of local recurrent networks may be facilitated by the fact that cortical neurons in the awake state have a low spontaneous rate of firing (a few Hz), which means that any small additional input may produce some spikes sooner than otherwise would have occurred, because some of the neurons may be very close to a threshold of firing.		0
Rolls; Memory, Attention, and Decision-Making	27		Typical quantitative estimates or neocortex (table)		1
Rolls; Memory, Attention, and Decision-Making	27		Local recurrent networks might also show some of the autoassociative retrieval of information typical of autoassociation networks, if the synapses between the nearby pyramidal cells have the appropriate Hebbian modifiability.		0
Rolls; Memory, Attention, and Decision-Making	27		The connection probability between hippocampal CA3 pyramidal cells is approximately 0.02--0.04, and this is thought to be sufficient to sustain associative retrieval.		0
Rolls; Memory, Attention, and Decision-Making	27		In the neocortex, each 1 mm2 region within which there is a relatively high density of recurrent collateral connections between pyramidal cells probably overlaps somewhat continuously with the next.		0
Rolls; Memory, Attention, and Decision-Making	27		The anatomy of local connections between nearby pyramidal cells and local inhibitory neurons may provide a network basis for understanding the columnar architecture of the neocortex, for it implies that local recurrent connectivity on this scale is a feature of the cortical computation.		0
Rolls; Memory, Attention, and Decision-Making	28		Functional Pathways through Cortical Layers		1
Rolls; Memory, Attention, and Decision-Making	29		Basic circuit for visual cortex (diagram)		1
Rolls; Memory, Attention, and Decision-Making	30		The forward corticocortical afferents to a cortical area sometimes have a columnar pattern it to their distribution with the column width 200-300 µ in diameter.		1
Rolls; Memory, Attention, and Decision-Making	30		Individual thalamocortical axons often end in patches in layer 4 which are 200-300 µ in diameter.		0
Rolls; Memory, Attention, and Decision-Making	30		The dendrites of spiny stellate cells are in the region of 500 µ in diameter, and their axons can distribute in patches 200-300 µ across, separated by distances up to 1 mm.		0
Rolls; Memory, Attention, and Decision-Making	30		Dendrites of layer 2 and 3 pyramidal cells can be approximately 300 µ in diameter.		0
Rolls; Memory, Attention, and Decision-Making	31		In the hierarchy of cortical stages, convergence and competition are key aspects of the processing.		1
Rolls; Memory, Attention, and Decision-Making	31		The hierarchy of cortical processing could act in a feedforward manner since it it has been shown there is insufficient time for top-down processing to occur when objects can just be recognized.		0
Rolls; Memory, Attention, and Decision-Making	31		Neurons in each cortical stage respond for 20-30 ms when an object can just be seen.		0
Rolls; Memory, Attention, and Decision-Making	31		Given that the time for processing to travel from V1 to inferior temporal visual cortex (IT) is approximately 50 ms, there is insufficient time for a return projection from IT to reach V1, influence the processing there, and in turn for V1 to project up to IT to alter the processing there.		0
Rolls; Memory, Attention, and Decision-Making	31		Backprojections are a major feature of cortical connectivity.		0
Rolls; Memory, Attention, and Decision-Making	32		Learning -- the way in which the back projections could assist learning in the cortex.		1
Rolls; Memory, Attention, and Decision-Making	34		Recall		2
Rolls; Memory, Attention, and Decision-Making	34		During recall, neural activity occurs in the cortical areas involved in the original processing.		0
Rolls; Memory, Attention, and Decision-Making	35		Semantic Priming		1
Rolls; Memory, Attention, and Decision-Making	35		Backprojection architecture could implement semantic priming by using the backprojecting neurons to provide a small activation of just those neurons that are appropriate for responding to the semantic category of input stimulus.		0
Rolls; Memory, Attention, and Decision-Making	35		Attention can operate from higher to lower levels, to selectively facilitate only certain pyramidal cells by using the backprojections.		0
Rolls; Memory, Attention, and Decision-Making	35		If the forward connections from one cortical area to the next, and the return backprojections, are both associatively modifiable, then the coupled networks could be regarded as, effectively, an autoassociative network.		0
Rolls; Memory, Attention, and Decision-Making	37		Hippocampus and Memory		2
Rolls; Memory, Attention, and Decision-Making	37		A theory of the hippocampus in which the CA3 neurons operate as an autoassociation memory to store episodic memories including object and place memories, and the dentate granule cells operate as a preprocessing stage by performing pattern separation so that the mossy fibers could act to set up different representations for each memory to be stored in the CA3 cells.		0
Rolls; Memory, Attention, and Decision-Making	37		Diluted connectivity of the recurrent collateral connections found in real biological networks.		0
Rolls; Memory, Attention, and Decision-Making	38		The primate hippocampus receives major inputs via the entorhinal cortex (area 28).		1
Rolls; Memory, Attention, and Decision-Making	38		The hippocampus is by its connections potentially able to associate together object representations (from the temporal lobe visual and auditory areas) and spatial representations.		0
Rolls; Memory, Attention, and Decision-Making	38		The entorhinal cortex receives inputs from the amygdala and the orbitofrontal cortex, which could provide reward related information to the hippocampus.		0
Rolls; Memory, Attention, and Decision-Making	39		The final convergence afforded by the hippocampus into one network in CA3 may be especially appropriate for episodic memory typically involving arbitrary associations between the any of the inputs of the hippocampus, e.g. spatial, vestibular related to self motion, visual object, olfactory, and auditory.		1
Rolls; Memory, Attention, and Decision-Making	39		There are also directs subcortical inputs to the hippocampus from the amygdala and septum.		0
Rolls; Memory, Attention, and Decision-Making	39		The primary output from the hippocampus to neocortex originates in CA1 and projects to subiculum, entorhinal cortex, and parahippocampal structures as well as prefrontal cortex.		0
Rolls; Memory, Attention, and Decision-Making	40		CA1 and subiculum also project via the fornix to subcortical areas such as the mammillary bodies and anterior thalamic nuclei.		1
Rolls; Memory, Attention, and Decision-Making	40		Damage to fornix connections to the hippocampus produced deficits in learning about the places of objects and about the places where responses should be made.		0
Rolls; Memory, Attention, and Decision-Making	41		Spatial processing involves a snapshot type of memory, in which one whole scene must be remembered.  This memory may then be a special case of episodic memory, which involves an arbitrary association of a particular set of events that describe a past episode.		1
Rolls; Memory, Attention, and Decision-Making	41		It is suggested that an autoassociation memory implemented by the CA3 neurons enables whole (spatial) scene or episodic memories to be formed, with a snapshot quality that depends on the arbitrary associations that can be made in the short temporal window that characterizes the synaptic modifiability in this system.		0
Rolls; Memory, Attention, and Decision-Making	41		The hypothesis is that the autoassociation memory enables arbitrary sets of concurrent neuronal firings, representing for example the spatial context where the episode occurred, the people present during the episode, and what was seen during the episode, to be associated together and stored as one event.		0
Rolls; Memory, Attention, and Decision-Making	41		Later recall of the episode from the hippocampus in response to a partial cue can then lead to reinstatement of the activity in the neocortex that was originally present during the episode.		0
Rolls; Memory, Attention, and Decision-Making	41		The autoassociation memory described here shows how the episodic memory could be stored in the hippocampus, and later retrieved from the hippocampus and thereby to the neocortex using backprojections.		0
Rolls; Memory, Attention, and Decision-Making	42		Experimental data show that recognition memory is not impaired by hippocampal damage.		1
Rolls; Memory, Attention, and Decision-Making	42		Spatial representation in the hippocampus of rats is about the place where the rat is, whereas the spatial representation in primates includes a representation of space "out there", as represented by spatial view cells.		0
Rolls; Memory, Attention, and Decision-Making	57		A theory of the operation of hippocampal circuitry as the memory system.		15
Rolls; Memory, Attention, and Decision-Making	58		Hippocampal circuitry		1
Rolls; Memory, Attention, and Decision-Making	58		CA3 as an Autoassociation Memory		0
Rolls; Memory, Attention, and Decision-Making	60		Representation of connections within the hippocampus (diagram)		2
Rolls; Memory, Attention, and Decision-Making	62		Architecture of an Attractor or Autoassociation neural network.  (diagram)		2
Rolls; Memory, Attention, and Decision-Making	62		A fundamental property of the autoassociation model of the CA3 recurrent collateral network is that it can implement a short-term memory by maintaining the firing of neurons using the excitatory recurrent collateral connections.		0
Rolls; Memory, Attention, and Decision-Making	62		A stable attractor can maintain one memory active for a considerable period, until a new input pushes the attractor to represent a new location or memory.		0
Rolls; Memory, Attention, and Decision-Making	63		An attractor network is one in which a stable pattern of firing is maintained once it has been started.		1
Rolls; Memory, Attention, and Decision-Making	67		Dynamics of the recurrent network.		4
Rolls; Memory, Attention, and Decision-Making	67		How quickly does a recurrent network settle into its final state?		0
Rolls; Memory, Attention, and Decision-Making	67		How long does it take the for a pattern of activity, originally evoked in CA3 by afferent inputs, become influenced by the activation of recurrent collaterals?		0
Rolls; Memory, Attention, and Decision-Making	68		Memory for Sequences		1
Rolls; Memory, Attention, and Decision-Making	71		Theta oscillations (5-8 Hz) and Gamma oscillations (30-80 Hz)		3
Rolls; Memory, Attention, and Decision-Making	72		Almost Poisson nature of the spike trains of single neurons found in most brain areas.		1
Rolls; Memory, Attention, and Decision-Making	72		Mossy Fiber Inputs to CA3 cells		0
Rolls; Memory, Attention, and Decision-Making	73		Mossy fibers may be necessary for new learning in the hippocampus, but may not be necessary for recall of existing memories from the hippocampus		1
Rolls; Memory, Attention, and Decision-Making	74		Perforant path inputs to CA3 cells		1
Rolls; Memory, Attention, and Decision-Making	74		Dynamics of the network is dominated by the randomizing effect of the recurrent collaterals.		0
Rolls; Memory, Attention, and Decision-Making	74		Associatively modifiable synapses are needed in the same order as the number of concurrently stored patterns, so that small cues can be effective.		0
Rolls; Memory, Attention, and Decision-Making	74		The perforant path system is the one involved in relaying the cues that initiate retrieval.		0
Rolls; Memory, Attention, and Decision-Making	74		The three major classes of excitatory input to the CA3 cells (recurrent collateral, mossy fiber, and perforant path) could be independently scaled by virtue of the different classes of inhibitory interneurons.		0
Rolls; Memory, Attention, and Decision-Making	75		Dentate granule cells		1
Rolls; Memory, Attention, and Decision-Making	75		The dentate granule cell stage of hippocampal processing, which precedes the CA3 stage, acts to produce during learning the sparse yet efficient representation in CA3 neurons that is required for the autoassociation to perform well.		0
Rolls; Memory, Attention, and Decision-Making	79		The learning of spatial view and place cell representations from visual inputs.		4
Rolls; Memory, Attention, and Decision-Making	81		Linking the inferior temporal visual cortex to spatial view and place cell representations.		2
Rolls; Memory, Attention, and Decision-Making	82		CA1 cells		1
Rolls; Memory, Attention, and Decision-Making	86		Backprojections to the neocortex		4
Rolls; Memory, Attention, and Decision-Making	88		Backprojections to the neocortex -- quantitative aspects		2
Rolls; Memory, Attention, and Decision-Making	94		CA3 subregion of the hippocampus		6
Rolls; Memory, Attention, and Decision-Making	95		The types of information associated in CA3		1
Rolls; Memory, Attention, and Decision-Making	96		Pattern Completion in CA3		1
Rolls; Memory, Attention, and Decision-Making	98		Recall In CA3		2
Rolls; Memory, Attention, and Decision-Making	98		Mossy fiber vs direct perforant path input to CA3		0
Rolls; Memory, Attention, and Decision-Making	99		Orthogonal representations in CA3		1
Rolls; Memory, Attention, and Decision-Making	100		Short-term memory, path integration, and CA3		1
Rolls; Memory, Attention, and Decision-Making	101		CA1 subregion of the hippocampus		1
Rolls; Memory, Attention, and Decision-Making	101		Sequence Memory and CA1		0
Rolls; Memory, Attention, and Decision-Making	102		Associations across time, and CA1		1
Rolls; Memory, Attention, and Decision-Making	103		Order memory, and CA1		1
Rolls; Memory, Attention, and Decision-Making	104		Intermediate memory, and CA1		1
Rolls; Memory, Attention, and Decision-Making	105		Evaluation of the theory of hippocampal function		1
Rolls; Memory, Attention, and Decision-Making	109		Comparison of other theories of hippocampal function		4
Rolls; Memory, Attention, and Decision-Making	113		Reward- and Punishment-related Learning -- Emotion and Motivation		4
Rolls; Memory, Attention, and Decision-Making	113		Emotions can be considered as states elicited by reinforcing stimuli, i.e. by rewards and punishers.		0
Rolls; Memory, Attention, and Decision-Making	113		We need to understand which stimuli are primary, i.e. unlearned, reinforcers; and we need to understand the associative processes that enable other previously neutral stimuli to come associated with these primary reinforcers.		0
Rolls; Memory, Attention, and Decision-Making	114		Primary Reinforces (table)		1
Rolls; Memory, Attention, and Decision-Making	116		Associative processes involved in reward and punishment related learning.		2
Rolls; Memory, Attention, and Decision-Making	116		Pavlovian or Classical Conditioning		0
Rolls; Memory, Attention, and Decision-Making	116		Stimulus-Response Association		0
Rolls; Memory, Attention, and Decision-Making	118		Conditioned Stimulus (CS) -- Unconditioned Stimulus (US) Associations		2
Rolls; Memory, Attention, and Decision-Making	119		Hedonic assessment		1
Rolls; Memory, Attention, and Decision-Making	120		Overtraining in an instrumental task results in instrumental behavior that becomes performed as a fixed inflexible habit.		1
Rolls; Memory, Attention, and Decision-Making	120		Stimulus -- Response Habits		0
Rolls; Memory, Attention, and Decision-Making	121		The main brain regions implicated in reward and punishment related learning include the amygdala, orbitofrontal cortex, cingulate cortex, and basal forebrain areas including the hypothalamus.		1
Rolls; Memory, Attention, and Decision-Making	122		Pathways involved in emotion (diagram)		1
Rolls; Memory, Attention, and Decision-Making	123		Connections of the taste, olfactory, somatosensory, and visual pathways in the brain (diagram)		1
Rolls; Memory, Attention, and Decision-Making	124		Representations of primary reinforcers.		1
Rolls; Memory, Attention, and Decision-Making	125		Taste		1
Rolls; Memory, Attention, and Decision-Making	125		Smell		0
Rolls; Memory, Attention, and Decision-Making	126		Pleasant and painful touch		1
Rolls; Memory, Attention, and Decision-Making	128		Visual stimuli		2
Rolls; Memory, Attention, and Decision-Making	128		Although most of visual stimuli are not primary reinforcers, but may become secondary reinforcers as a result of stimulus-reinforcer autoassociation learning, it is possible that some visual stimuli, such as the sight of a smiling face or of an angry face, could be primary reinforcers.		0
Rolls; Memory, Attention, and Decision-Making	128		For humans, the reinforcing value of face expression could be the decoded by the cortical processing of the inferior temporal visual cortex.		0
Rolls; Memory, Attention, and Decision-Making	128		The inferior temporal visual cortex also projects into the amygdala, in which face-selective neurons are also found.		0
Rolls; Memory, Attention, and Decision-Making	128		A population of face-selective neurons is also found in the orbitofrontal cortex.		0
Rolls; Memory, Attention, and Decision-Making	129		Orbitofrontal and cingulate cortex lesions can impair humans' ability to identify the emotional expression on a face.		1
Rolls; Memory, Attention, and Decision-Making	129		Some of the face-selective neurons in the amygdala and orbitofrontal cortex reflect the secondary reinforcing value of the face, given the role these brain regions play in stimulus-reinforcer association learning.		0
Rolls; Memory, Attention, and Decision-Making	129		In humans, it is then found that activation of the orbitofrontal cortex is correlated with the attractiveness of the face.		0
Rolls; Memory, Attention, and Decision-Making	129		It is possible that some auditory stimuli can be primary reinforcers. Where the reinforcement may be decoded is not yet known, though auditory neurons that respond to vocalization have been found in orbitofrontal cortex and amygdala, and may also be present in the cingulate cortex.		0
Rolls; Memory, Attention, and Decision-Making	129		Orbitofrontal and cingulate cortex lesions can impair humans ability to identify the emotional expression in a voice.		0
Rolls; Memory, Attention, and Decision-Making	129		Novel stimuli are somewhat rewarding and in this sense act as primary reinforcers. The value of this type of reinforcer is that it encourages animals to explore new environments in which their genes might produce a fitness advantage.		0
Rolls; Memory, Attention, and Decision-Making	129		Neurons that respond to visual stimuli that are associated with rewards, and to novel stimuli, have been discovered in the primate amygdala, and this evidence suggests that the neurons are involved in the primary reinforcing properties of novel stimuli.		0
Rolls; Memory, Attention, and Decision-Making	129		Representing potential secondary reinforcers.		0
Rolls; Memory, Attention, and Decision-Making	131		Generalization -- if we learn an emotional response to an object, we usually want to generalize the emotional response to other similar objects.		2
Rolls; Memory, Attention, and Decision-Making	132		Graceful degradation		1
Rolls; Memory, Attention, and Decision-Making	133		Objects, and not their reward and punishment associations, are represented in the inferior temporal visual cortex.		1
Rolls; Memory, Attention, and Decision-Making	135		Why reward and punishment associations of stimuli are not represented early in the information processing in the primate brain.		2
Rolls; Memory, Attention, and Decision-Making	136		Orbitofrontal Cortex		1
Rolls; Memory, Attention, and Decision-Making	136		Phineas Gage		0
Rolls; Memory, Attention, and Decision-Making	137		Prefrontal leucotomy		1
Rolls; Memory, Attention, and Decision-Making	140		Effects of damage to the orbitofrontal cortex.		3
Rolls; Memory, Attention, and Decision-Making	142		Orbitofrontal cortex contains a major cortical representation of taste.		2
Rolls; Memory, Attention, and Decision-Making	147		An olfactory representation in the orbitofrontal cortex.		5
Rolls; Memory, Attention, and Decision-Making	151		Flavor representation -- convergence of taste and olfactory inputs in the orbitofrontal cortex.		4
Rolls; Memory, Attention, and Decision-Making	153		Visual inputs to the orbitofrontal cortex.		2
Rolls; Memory, Attention, and Decision-Making	153		Visual stimulus-reinforcer association learning and reversal.		0
Rolls; Memory, Attention, and Decision-Making	153		There is a major visual input to many neurons in the orbitofrontal cortex, and what is represented by these neurons in many cases is the reinforcer (reward all punisher) association of visual stimuli.  The primary reinforcer that has been used is taste.		0
Rolls; Memory, Attention, and Decision-Making	153		The orbitofrontal cortex visual neurons in many cases reverse their responses to visual stimuli when the taste with which the visual stimuli stimulus is associated is reversed.		0
Rolls; Memory, Attention, and Decision-Making	164		A representation of faces in the orbitofrontal cortex.		11
Rolls; Memory, Attention, and Decision-Making	164		There is a population of orbitofrontal cortex face-selective neurons that respond in many ways similar to those in the temporal cortical visual areas.		0
Rolls; Memory, Attention, and Decision-Making	165		Orbitofrontal face responsive neurons tend to respond with longer latencies (130-220 ms typically) than temporal lobe neurons (80-100 ms). They also convey information about which face is being seen, by having different responses to different faces.		1
Rolls; Memory, Attention, and Decision-Making	165		Some of the face identity neurons in similar responses to different views of a face, which is a useful property of neurons responding to face identity.		0
Rolls; Memory, Attention, and Decision-Making	165		Some face neurons respond to moving but not still heads.		0
Rolls; Memory, Attention, and Decision-Making	165		Some face neurons are tuned to face expression.		0
Rolls; Memory, Attention, and Decision-Making	165		Neurons with face expression, face movement, or face view-dependent responses would all be useful as part of a system decoding and representing signals important in social interactions.		0
Rolls; Memory, Attention, and Decision-Making	166		The representation of face identity is also important and social interactions, for it provides some of the information needed in order to make different responses to different individuals.		1
Rolls; Memory, Attention, and Decision-Making	166		Some orbitofrontal cortex neurons were shown to be tuned to auditory stimuli, including for some neurons the sound of vocalizations.		0
Rolls; Memory, Attention, and Decision-Making	166		Activation of the lateral orbitofrontal cortex occurs when a rewarding smile expression is expected, but an angry face expression is obtained, a visual discrimination reversal.		0
Rolls; Memory, Attention, and Decision-Making	166		Activation of the orbitofrontal cortex is found when humans are making social judgments.		0
Rolls; Memory, Attention, and Decision-Making	166		Auditory stimuli may have similar representations in the orbitofrontal cortex related to their affective value.		0
Rolls; Memory, Attention, and Decision-Making	166		Researchers have found a correlation between subjective ratings of dissonance and consonance of musical chords in the activations produced in the orbitofrontal cortex.		0
Rolls; Memory, Attention, and Decision-Making	166		Affective states, moods, can influence cognitive processing, including perception and memory.		0
Rolls; Memory, Attention, and Decision-Making	166		Cognitive processing events, if decoded as being rewarding or punishing, can produce emotional states.		0
Rolls; Memory, Attention, and Decision-Making	175		Human orbitofrontal cortex.		9
Rolls; Memory, Attention, and Decision-Making	183		Orbitofrontal cortex has a key role in representing primary reinforcers, and learning and rapidly changing associations between stimuli and primary reinforcers, and thus is important in many types of emotional and motivational behavior.		8
Rolls; Memory, Attention, and Decision-Making	183		Individual differences in the functioning of the orbitofrontal cortex may contribute to differences in personality, the psychiatric states, and to differences in the rewards that drive people to action.		0
Rolls; Memory, Attention, and Decision-Making	187		Amygdala		4
Rolls; Memory, Attention, and Decision-Making	188		Connections to the amygdala.		1
Rolls; Memory, Attention, and Decision-Making	189		Connections to the amygdala, monkey brain (diagram)		1
Rolls; Memory, Attention, and Decision-Making	190		Effects of amygdala lesions.		1
Rolls; Memory, Attention, and Decision-Making	196		Neuronal activity in the primate amygdala that the reinforcing stimuli.		6
Rolls; Memory, Attention, and Decision-Making	201		LeDoux has described a theory of the neural basis of emotion which is conceptually similar to that of Rolls, except that he focuses mostly on the role of the amygdala in emotion (and not on other brain regions such is the orbitofrontal cortex); except that he focuses mainly on fear; and except that he suggests from neurophysiological findings that an important route for conditioned emotional stimuli to influence behavior is via the subcortical inputs (especially from the medial part of the medial geneticulate nucleus of the thalamus) to the amygdala.		5
Rolls; Memory, Attention, and Decision-Making	201		Humans and other animals do not generally want to learn that a particular pure tone is associated with the reward or punishment.  Instead, it might be that a particular complex pattern of sounds such as a vocalization carries a reinforcement signal, and this may be independent of the exact pitch at which it is uttered.		0
Rolls; Memory, Attention, and Decision-Making	202		Responses of these amygdala neurons to reinforcing and novel stimuli.		1
Rolls; Memory, Attention, and Decision-Making	204		Neuronal responses in the amygdala to faces.		2
Rolls; Memory, Attention, and Decision-Making	211		Cingulate Cortex		7
Rolls; Memory, Attention, and Decision-Making	212		Anterior or perigenual cingulate cortex, reward, and affect.		1
Rolls; Memory, Attention, and Decision-Making	216		A current working hypothesis is that the affective part of the anterior cingulate cortex receives inputs about expected rewards and punishers, and about the  rewards and punishers received, from the orbitofrontal cortex and amygdala.		4
Rolls; Memory, Attention, and Decision-Making	216		The anterior cingulate cortex may act as an output system for emotional responses and actions.		0
Rolls; Memory, Attention, and Decision-Making	217		Mid-cingulate cortex, the cingulate motor area, and action-outcome learning.		1
Rolls; Memory, Attention, and Decision-Making	217		The mid-cingulate area may be divided into an anterior or rostral cingulate motor area concerned with skeletomotor control which may be required in avoidance and fear, and a posterior or caudal cingulate motor area which may be more involved in skeletomotor orientation.		0
Rolls; Memory, Attention, and Decision-Making	218		Brain mechanisms of action-outcome learning may involve the anterior cingulate cortex.		1
Rolls; Memory, Attention, and Decision-Making	218		Reward and punisher-related information may reach the mid-cingulate area, where it can be associated with skeletomotor response representations.		0
Rolls; Memory, Attention, and Decision-Making	218		It is useful to place action-outcome learning and a wider context of emotion.  When stimuli are presented, they are decoded to determine whether they are primary rewards or punishers by structures such as the orbitofrontal cortex and amygdala.		0
Rolls; Memory, Attention, and Decision-Making	218		When reinforcer contingencies change, the orbitofrontal cortex is important in reversing the stimulus-stimulus association, and contains appropriate error neurons.  The orbitofrontal cortex is involved in this way in decision-making and executive function.		0
Rolls; Memory, Attention, and Decision-Making	219		Human brain imaging investigations of mood and depression.		1
Rolls; Memory, Attention, and Decision-Making	220		Output pathways for reward- and punisher-guided behavior, including emotional responses.		1
Rolls; Memory, Attention, and Decision-Making	220		Autonomic and endocrine systems		0
Rolls; Memory, Attention, and Decision-Making	221		Motor systems for implicit responses, including the basal ganglia, reinforcement learning, and dopamine.		1
Rolls; Memory, Attention, and Decision-Making	221		Systems-level architecture of the basal ganglia.		0
Rolls; Memory, Attention, and Decision-Making	224		System-level analysis of the basal ganglia -- neuronal activity in different parts of the striatum.		3
Rolls; Memory, Attention, and Decision-Making	225		Ventral Striatum		1
Rolls; Memory, Attention, and Decision-Making	230		Tail of the caudate nucleus, and posteroventral putamen.		5
Rolls; Memory, Attention, and Decision-Making	231		Postero-ventral putamen		1
Rolls; Memory, Attention, and Decision-Making	232		Head of the caudate nucleus		1
Rolls; Memory, Attention, and Decision-Making	237		Computations performed by the basal ganglia.		5
Rolls; Memory, Attention, and Decision-Making	243		Dopamine, reinforcement, and reinforcement learning.		6
Rolls; Memory, Attention, and Decision-Making	245		Dopamine neurons could not convey information about a primary reward obtained if the trial is successful, in the way that orbitofrontal cortex neurons do.		2
Rolls; Memory, Attention, and Decision-Making	249		Basal forebrain and hypothalamus		4
Rolls; Memory, Attention, and Decision-Making	249		Basal forebrain cholinergic neurons		0
Rolls; Memory, Attention, and Decision-Making	251		Noradrenergic neurons		2
Rolls; Memory, Attention, and Decision-Making	252		Opiate reward systems, analgesia, and food reward.		1
Rolls; Memory, Attention, and Decision-Making	253		Effects of emotion on cognitive processing and memory.		1
Rolls; Memory, Attention, and Decision-Making	254		Backprojections from the primate amygdala to the cortex spread more widely than the afferent connections, which for vision come mainly from the inferior temporal visual cortical areas.		1
Rolls; Memory, Attention, and Decision-Making	255		Pyramidal cells in layers 2 and 3 of the temporal lobe association cortex receive forward inputs from preceding cortical stages of processing, and also backprojections from the amygdala and orbitofrontal cortex. (diagram)		1
Rolls; Memory, Attention, and Decision-Making	255		Theory of how the effects of mood on memory and perception could be implemented in the brain. Massive backprojections from parts of the brain where mood is represented, such it the orbitofrontal cortex and amygdala, to the cortical areas such as the inferior temporal visual cortex and hippocampus-related areas that project into these mood-representing areas.		0
Rolls; Memory, Attention, and Decision-Making	255		The computer model of the effects of mood on memory and perception uses an attractor network in the mood module, which helps the mood to be an enduring state, and also an attractor in the inferior temporal visual cortex (IT).		0
Rolls; Memory, Attention, and Decision-Making	255		The computer model of mood the system is treated as a system of couple attractors with many different perceptual states associated with any one moods state.		0
Rolls; Memory, Attention, and Decision-Making	255		Overall, there is a large number of perceptual/memory states, and only a few moods states, so that there is a many-to-one relation between perceptual/memory states and the associated moods states.		0
Rolls; Memory, Attention, and Decision-Making	255		The mood computer model reveals the ability of a perceptual input to trigger a mood state in the amygdala if there is not an existing mood, but greater difficulty to induce a new mood if there is already a strong mood attractor present.		0
Rolls; Memory, Attention, and Decision-Making	256		General effects needed for forward and backward projections in the brain -- forward projections must be relatively strong to produce new firing in a module when a new (forward) input is received, and backward projections must be relatively weak, if they are to mildly implement top-down constraints without dominating the activity of the earlier modules.		1
Rolls; Memory, Attention, and Decision-Making	257		Laterality effects in human reward and emotional processing.		1
Rolls; Memory, Attention, and Decision-Making	257		Damage to the left hemisphere (in right-handed people) is more likely to affect language, and to the right hemisphere is more likely to affect emotional processing.		0
Rolls; Memory, Attention, and Decision-Making	257		Greater probability of impairments in recognizing facial expressions after right rather than left hemisphere damage.		0
Rolls; Memory, Attention, and Decision-Making	257		Patients are more likely to be depressed by a stroke if it is to the left than to the right hemisphere.		0
Rolls; Memory, Attention, and Decision-Making	257		The result for strokes may indicate that to feel depressed, the right hemisphere is normally involved.		0
Rolls; Memory, Attention, and Decision-Making	257		Experience with split-brain patients provides evidence that processing of implicit information about emotion can be dissociated from explicit, language-based systems.		0
Rolls; Memory, Attention, and Decision-Making	257		Face identification deficits are not necessarily associated with face expression identification impairments.		0
Rolls; Memory, Attention, and Decision-Making	257		Why should there be some lateralization of emotional processing in humans?  One argument is that whenever a function does not need to be represented bilaterally due to the topology of the body, then it is more efficient to place the group of neurons concerned with that processing close together.		0
Rolls; Memory, Attention, and Decision-Making	258		An advantage of genetic specification of connectivity between neuron types, and of keeping neurons concerned with the same computation close together, is that this minimizes the problem of guidance of axons toward their targets.		1
Rolls; Memory, Attention, and Decision-Making	258		Some distant parts of the brain that are connected in adults are connected because the connections can be made early in development, before the different brain regions have migrated to become distant.		0
Rolls; Memory, Attention, and Decision-Making	258		Wherever possible, neurons performing the same computation should be close together.		0
Rolls; Memory, Attention, and Decision-Making	258		Where there is no body-symmetry reason to have separate representations for each side of the body, the representation would optimally be lateralized.		0
Rolls; Memory, Attention, and Decision-Making	262		Invariant visual object recognition learning.		4
Rolls; Memory, Attention, and Decision-Making	262		Invariant representations of faces and objects in the inferior temporal visual cortex.		0
Rolls; Memory, Attention, and Decision-Making	263		Schematic diagram of the visual pathways from the retina to the visual cortical areas (diagram)		1
Rolls; Memory, Attention, and Decision-Making	263		Processing to the inferior temporal cortex in the primate visual system.		0
Rolls; Memory, Attention, and Decision-Making	264		Translation invariance and receptive field size.		1
Rolls; Memory, Attention, and Decision-Making	268		Size and spatial frequency invariance.		4
Rolls; Memory, Attention, and Decision-Making	269		Combination of features in the correct spatial configuration.		1
Rolls; Memory, Attention, and Decision-Making	270		A view-invariant representation.		1
Rolls; Memory, Attention, and Decision-Making	274		Learning of new representations in the temporal cortical visual areas.		4
Rolls; Memory, Attention, and Decision-Making	275		Rapid learning of representations of new objects appears to be a major type of learning in which the temporal cortical areas are involved.		1
Rolls; Memory, Attention, and Decision-Making	276		Distributed encoding		1
Rolls; Memory, Attention, and Decision-Making	277		Responses of a face-selective neuron in the inferior temporal visual cortex to four different faces (diagram)		1
Rolls; Memory, Attention, and Decision-Making	281		Face expression, gesture and view represented in a population of neurons in the cortex in the inferior temporal sulcus.		4
Rolls; Memory, Attention, and Decision-Making	281		Specialized regions in the temporal cortical visual areas.		0
Rolls; Memory, Attention, and Decision-Making	283		Neuroimaging data provides evidence consistent with neurophysiology that they are different face processing systems in the human brain, and different processing subsystems for objects, moving objects, and scenes.		2
Rolls; Memory, Attention, and Decision-Making	284		The amygdala and orbitofrontal cortex may become activated particularly by certain face expressions.		1
Rolls; Memory, Attention, and Decision-Making	284		Different inferior temporal cortex neurons respond differently to different categories of visual stimuli, some neurons conveying information primarily about faces, and others about objects.		0
Rolls; Memory, Attention, and Decision-Making	284		In the inferior temporal cortex, there are small clustered groups of neurons, with neurons within a cluster responding to somewhat similar attributes of stimuli, and different clusters responding to different categories or types of visual stimuli.		0
Rolls; Memory, Attention, and Decision-Making	299		VisNet is a model of invariant object recognition based on Edmund Rolls's hypothesis.  It is a computer simulation that allows hypotheses to be tested and developed about how multilayer hierarchical networks of a type believed to be implemented in the visual cortex pathways operate.		15
Rolls; Memory, Attention, and Decision-Making	299		The VisNet model, as with all models, requires precise specification for what is to be implemented, and it is same time involves specified simplifications of the real architecture, as investigations of the fundamental aspects of the information processing being performed are more tractable in a simplified and at the same time quantitatively specified model.		0
Rolls; Memory, Attention, and Decision-Making	299		The VisNet model contains a series a competitive networks, organized in our hierarchical layers, exhibiting mutual inhibition over a short range within each layer.  These networks allow combinations of features or inputs occurring in a given spatial arrangement to be learned by neurons, ensuring that higher order spatial properties of the input stimuli are represented in the network.		0
Rolls; Memory, Attention, and Decision-Making	299		The VisNet model has a convergent series of connections from a localized population of cells in preceding layers to each cell of the following layer, thus allowing the receptive field size of cells to increase through the visual processing areas all layers.		0
Rolls; Memory, Attention, and Decision-Making	299		The VisNet model uses a modified Hebb-like learning rule incorporating a temporal trace of each cell's previous activity, which will enable the neurons to learn transform invariances.		0
Rolls; Memory, Attention, and Decision-Making	299		The learning rule implemented in VisNet simulation utilizes the spatio-temporal constraints placed upon the behavior of real world objects to learn about natural object transformations.  By presenting consistent sequences of transforming objects the cells in the network can learn to respond to the same object through all of its naturally transformed states.		0
Rolls; Memory, Attention, and Decision-Making	301		The VisNet network consists of a series of four layers, constructed such that the convergence of information from the most disparate parts of the network's input layer can potentially influence firing in a single neuron in the final layer.		2
Rolls; Memory, Attention, and Decision-Making	324		The issue of feature binding, and a solution.		23
Rolls; Memory, Attention, and Decision-Making	325		Syntactic binding of separate neuronal assemblies by synchronization.		1
Rolls; Memory, Attention, and Decision-Making	325		Synchronization to implement syntactic binding has a number of disadvantages and limitations.		0
Rolls; Memory, Attention, and Decision-Making	325		Synchronization does not by itself define the spatial relations between the features being bound, so is not, just as binding mechanism, adequate for shape recognition.		0
Rolls; Memory, Attention, and Decision-Making	325		When stimulus-dependent temporal synchronization has been rigorously tested with information theoretic approaches, it is then found that most of the information available is in the number of spikes, with rather little, less than 5% of the total information, in stimulus-dependent synchronization.		0
Rolls; Memory, Attention, and Decision-Making	325		The implication of research results is that any stimulus dependent synchrony that is present is not quantitatively important as measured by information theoretic analyses under natural scene conditions when feature binding, segregation of objects in the background, and attention are required.		0
Rolls; Memory, Attention, and Decision-Making	325		The disadvantages of synchronization have been found for the inferior temporal cortex, a brain region where features are put together to form a representations of objects, and where attention has strong effects.		0
Rolls; Memory, Attention, and Decision-Making	325		In connection with the rate codes, it should be noted that a rate code implies using the number of spikes that arrive in a given time, and that this time can be very short, as little as 20-50 ms, for very useful amounts of information to be made available from a population of neurons.		0
Rolls; Memory, Attention, and Decision-Making	326		In the context of the visual simulation program VisNet, and how the real visual system may operate to implement object recognition, the use of synchronization does not appear to match the way in which the visual system is organized.		1
Rolls; Memory, Attention, and Decision-Making	326		The primate uses a considerable part of its cortex, perhaps 50% in monkeys, or visual processing.		0
Rolls; Memory, Attention, and Decision-Making	326		The solution adopted by the real visual system may be one which relies on many neurons with simpler processing than arbitrary syntax implemented by synchronous firing of separate assemblies suggests.		0
Rolls; Memory, Attention, and Decision-Making	326		A network that forms low-order combinations of features represented in previous layers, is very demanding in terms of the number of neurons required, and this matches what is found in the primate visual system.		0
Rolls; Memory, Attention, and Decision-Making	326		Binding of features and their relative spatial position by feature combination neurons.		0
Rolls; Memory, Attention, and Decision-Making	326		By forming neurons that respond to combinations of a few features in the correct spatial arrangement, the advantages for syntactic binding are obtained, yet without the combinatorial explosion that would result if the feature combination neurons responded to combinations of many input features so producing potentially very specifically tuned neurons that very rarely responded.		0
Rolls; Memory, Attention, and Decision-Making	326		Invariant representations are developed in the next layer from these feature combination neurons which already contain evidence on their local spatial arrangement of features.		0
Rolls; Memory, Attention, and Decision-Making	326		Finally, in later layers, only one stimulus would be specified by the particular set of low order featured combination neurons present, even though each feature combination neuron would itself be somewhat invariant.		0
Microphone	373		The full view-invariant recognition of objects that occurs even when the objects share the same features, such as color, texture, etc., is an especially computationally demanding task, which the primate visual system is able to perform with its highly developed temporal lobe cortical visual areas.		47
Rolls; Memory, Attention, and Decision-Making	375		There are a number of different short-term memory systems, each implemented in a different cortical area.		2
Rolls; Memory, Attention, and Decision-Making	375		A particular kind of short-term memory can be implemented with subpopulations of neurons that show maintained activity in a delay period, while a stimulus or event is being remembered.		0
Rolls; Memory, Attention, and Decision-Making	375		This kind of short-term memory may operate as an autoassociative attractor network.		0
Rolls; Memory, Attention, and Decision-Making	375		The actual autoassociation could be implemented by autoassociatively modifiable synapses between connected pyramidal cells within an area, or by the forward and backward connections between adjacent cortical areas in a hierarchy.		0
Rolls; Memory, Attention, and Decision-Making	375		One short-term memory system is in the dorsolateral prefrontal cortex, area 46.		0
Rolls; Memory, Attention, and Decision-Making	375		There is an analogous short-term memory system in a more dorsal and posterior part of the prefrontal cortex involved in remembering the position in visual space to which eye movement (a saccade) should be made.		0
Rolls; Memory, Attention, and Decision-Making	375		Another short-term memory system is implemented in inferior temporal visual cortex, especially more ventrally toward their perirhinal cortex.		0
Rolls; Memory, Attention, and Decision-Making	377		Another short-term memory system is human auditory--verbal short-term memory, which appears to be implemented in the left hemisphere at the junction of the temporal, parietal, and occipital lobes.		2
Rolls; Memory, Attention, and Decision-Making	377		Patients with damage to the auditory--verbal short-term memory system are described clinically as showing conduction aphasia, in that they cannot repeat a heard string of words.		0
Rolls; Memory, Attention, and Decision-Making	378		Prefrontal cortex short-term memory networks, and their relation to temporal and parietal perception networks		1
Rolls; Memory, Attention, and Decision-Making	378		A common way that the brain uses to implement a short-term memory is to maintain the firing of neurons during a short memory period after the end of a stimulus.		0
Rolls; Memory, Attention, and Decision-Making	378		In the inferior temporal cortex, this firing may be maintained for a few hundred milliseconds.		0
Rolls; Memory, Attention, and Decision-Making	378		In more ventral temporal cortical areas such as the entorhinal cortex, the firing may be maintained for longer periods.		0
Rolls; Memory, Attention, and Decision-Making	378		In the dorsolateral and inferior convexity prefrontal cortex the firing of the neurons may be related to the memory of spatial responses or objects or both.		0
Rolls; Memory, Attention, and Decision-Making	378		The firing may be maintained by the operation of associatively modified recurrent collateral connections between the nearby pyramidal cells producing attractor states in autoassociative networks.		0
Rolls; Memory, Attention, and Decision-Making	378		For the short-term memory to be maintained during periods in which new stimuli are to be perceived, there must be separate networks for the perceptual and short-term memory functions.		0
Rolls; Memory, Attention, and Decision-Making	378		Two coupled networks, one in the inferior temporal visual cortex for perceptual functions, and another in the prefrontal cortex for maintaining the short-term memory during intervening stimuli, provide a precise model for the interaction of perceptual and short-term memory systems.		0
Rolls; Memory, Attention, and Decision-Making	378		The ability to provide multiple separate short-term attractor memories provides the basis for the dorsolateral prefrontal cortex functions in planning.		0
Rolls; Memory, Attention, and Decision-Making	379		A short-term memory autoassociation network in the prefrontal cortex could hold active a working memory representation by maintaining its firing in an attractor state. (diagram)		1
Rolls; Memory, Attention, and Decision-Making	383		Computational necessity for a separate, prefrontal cortex, short-term memory system.		4
Rolls; Memory, Attention, and Decision-Making	399		Attentional effects in the absence of visual stimulation		16
Rolls; Memory, Attention, and Decision-Making	401		A basic computational module for biased competition		2
Rolls; Memory, Attention, and Decision-Making	402		Neurodynamical architecture of a model of spatial and object-based attention.		1
Rolls; Memory, Attention, and Decision-Making	411		Role of attention and object recognition and visual search.		9
Rolls; Memory, Attention, and Decision-Making	414		Dynamics of spatial attention and object recognition.		3
Rolls; Memory, Attention, and Decision-Making	416		Dynamics of object attention and visual search.		2
Rolls; Memory, Attention, and Decision-Making	417		Neuronal and biophysical mechanisms of attention.		1
Rolls; Memory, Attention, and Decision-Making	421		Serial versus Parallel search.		4
Rolls; Memory, Attention, and Decision-Making	421		Preattentive pop-out		0
Rolls; Memory, Attention, and Decision-Making	424		Binding problem in perception -- how we specify what goes with what and where, in order to achieve the perception of a coherent integration of shape, color, movement, and size in individual objects.		3
Rolls; Memory, Attention, and Decision-Making	429		Linking computational and neurophysiological data on attention.		5
Rolls; Memory, Attention, and Decision-Making	429		The neglect syndrome.		0
Rolls; Memory, Attention, and Decision-Making	430		A model of visual spatial neglect.		1
Rolls; Memory, Attention, and Decision-Making	437		Disengagement of attention in neglect.		7
Rolls; Memory, Attention, and Decision-Making	438		Extinction and visual search.		1
Rolls; Memory, Attention, and Decision-Making	442		In the attention model, control is performed by the information loaded into prefrontal cortex short-term memories, biasing early visual cortical spatial and object processing areas by backprojections.		4
Rolls; Memory, Attention, and Decision-Making	442		Auditory -- verbal short-term memory system in humans (which using rehearsal holds on-line a set of approximately 7 chunks of information), in which may be located in the cortex at the junction of the left parieto-occipito-temporal areas.		0
Rolls; Memory, Attention, and Decision-Making	524		Dual routes to the initiation of actions in response to rewarding and punishing stimuli. (diagram)		82
Rolls; Memory, Attention, and Decision-Making	543		Neural Network Models		19
Rolls; Memory, Attention, and Decision-Making	543		Pattern association memory		0
Rolls; Memory, Attention, and Decision-Making	543		Neural network models are needed in order to provide a basis for understanding the processing and memory functions performed by neuronal networks in the brain.		0
Rolls; Memory, Attention, and Decision-Making	543		Neural networks used by the parts of the brain involved in memory, attention, decision-making, and the building of perceptual representations.		0
Rolls; Memory, Attention, and Decision-Making	543		Understanding the neural network models provides a basis for understanding the theories of how different types of memory functions are performed.		0
Rolls; Memory, Attention, and Decision-Making	543		Synaptic plasticity and the rules by which synaptic strength is modified is based on studies with long-term potentiation.		0
Rolls; Memory, Attention, and Decision-Making	543		Network models emphasized here utilize a local learning rule, i.e. a rule for synaptic modification, in which the signals needed to alter the synaptic strength are present in pre- and post-synaptic neurons.		0
Rolls; Memory, Attention, and Decision-Making	543		The issue of biological plausibility endorses the networks described here, contrasting with less biologically plausible networks such as multilayer backpropagation of error networks.		0
Rolls; Memory, Attention, and Decision-Making	545		Learning		2
Rolls; Memory, Attention, and Decision-Making	546		Recall		1
Rolls; Memory, Attention, and Decision-Making	552		The importance of distributed representations for pattern associators.		6
Rolls; Memory, Attention, and Decision-Making	552		A distributed representation is one which the firing or activity of all elements in the vector is used to encode a particular stimulus.		0
Rolls; Memory, Attention, and Decision-Making	552		A local representation is one in which all of the information that a particular stimulus or event has occurred is provided by the activity of one of the neurons or elements in the vector.		0
Rolls; Memory, Attention, and Decision-Making	552		If a single-cell local representation did exist it might be called a "grandmother cell."		0
Rolls; Memory, Attention, and Decision-Making	552		When the activity of a number of cells must be taken into account to represent a stimulus, then the representation is sometimes described as using ensemble encoding.		0
Rolls; Memory, Attention, and Decision-Making	552		For associative memories, generalization and graceful degradation are only implemented if the representation is distributed.		0
Rolls; Memory, Attention, and Decision-Making	552		Graceful degradation and generalization are dependent on distributed representations.		0
Rolls; Memory, Attention, and Decision-Making	553		Speed		1
Rolls; Memory, Attention, and Decision-Making	553		Recall is very fast in a real neural network, because the conditioned stimulus input firings can be applied simultaneously to the synapses, and the activation can be accumulated in one or two time constants of the dendrite (e.g. 10-20 ms)		0
Rolls; Memory, Attention, and Decision-Making	553		Learning is fast ('one-shot') and pattern associators, in that a single pairing of the conditioned stimulus and the unconditioned stimulus enables the association to be learned. There is no need to repeat the pairing in order to discover over many trials the appropriate mapping.		0
Rolls; Memory, Attention, and Decision-Making	553		Co-occurrence or near simultaneity of CS and the UCS is required for periods of his little as 100 ms, with the expression of the synaptic modification being present within typically a few seconds.		0
Rolls; Memory, Attention, and Decision-Making	560		Autoassociation or Attractor Memory		7
Rolls; Memory, Attention, and Decision-Making	560		Autoassociative memories or attractor neural networks, store memories, each one of which is represented by a pattern of neural activity.		0
Rolls; Memory, Attention, and Decision-Making	560		The memories of autoassociative or attractor neural networks are stored in the recurrent synaptic connections between the neurons of the network.		0
Rolls; Memory, Attention, and Decision-Making	560		Autoassociative networks can recall the appropriate memory from the network when provided with a fragment of one of the memories.		0
Rolls; Memory, Attention, and Decision-Making	560		A feature of autoassociative memory is that it is content addressable, i.e. the information in the memory can be accessed if just the contents of the memory (or a part of the contents of the memory) are used.		0
Rolls; Memory, Attention, and Decision-Making	560		Autoassociation memory can be used as a short-term memory, and which iterative processing around the recurrent collateral connection little keeps a representation active by continuing neuronal firing.		0
Rolls; Memory, Attention, and Decision-Making	560		The short-term memory reflected in continuing neuronal firing for several hundred milliseconds after a visual stimulus is removed, which is president in visual cortical areas such as the inferior temporal visual cortex, is probably implemented by autoassociation memory.		0
Rolls; Memory, Attention, and Decision-Making	560		Autoassociation memories also appeared to be used in a short-term memory role in the prefrontal cortex.		0
Rolls; Memory, Attention, and Decision-Making	560		The temporal visual cortical areas have connections to the ventral lateral prefrontal cortex which help to implement the short-term memory for visual stimuli.		0
Rolls; Memory, Attention, and Decision-Making	560		The parietal cortex has connections to the dorsolateral prefrontal cortex for the short-term memory of spatial responses.		0
Rolls; Memory, Attention, and Decision-Making	560		The short-term memory connections between prefrontal cortex, temporal visual cortical areas, and parietal cortex provide a mechanism that enables attention to be maintained through backprojections from the prefrontal cortex areas to the temporal and parietal areas that send projections to the prefrontal cortex.		0
Rolls; Memory, Attention, and Decision-Making	560		Autoassociation networks implemented by recurrent collateral synapses between cortical pyramidal cells provide a mechanism for constraint satisfaction and also noise reduction, whereby the firing of neighboring neurons can be taken into account in enabling the network to settle into a state that reflects all of the details of the inputs activating the population of connected neurons.
Rolls; Memory, Attention, and Decision-Making	560		Attractor networks are also effectively implemented by virtue of the forward and backward connections between cortical areas.
Rolls; Memory, Attention, and Decision-Making	560		An autoassociation network with rapid synaptic plasticity can learn each memory in one trial.
Rolls; Memory, Attention, and Decision-Making	560		Because of its 'one-shot' rapid learning, and ability to complete, an autoassociation network is well suited for episodic memory storage, in which each past episode must be stored and recalled later from a fragment, and kept separate from other episodic memories.
Rolls; Memory, Attention, and Decision-Making	562		Analysis of the operation of autoassociation networks		2
Rolls; Memory, Attention, and Decision-Making	564		Completion  [Gestalts]		2
Rolls; Memory, Attention, and Decision-Making	564		Perhaps the most important and useful property of autoassociation networks is that they complete an incomplete input vector allowing recall of a whole memory from a fraction of it.		0
Rolls; Memory, Attention, and Decision-Making	564		Graceful Degradation or Fault Tolerance		0
Rolls; Memory, Attention, and Decision-Making	565		Prototype extraction, extraction of central tendency, and noise reduction		1
Rolls; Memory, Attention, and Decision-Making	565		Distributed information storage in a neuronal network such as an autoassociator stores the autocorrelation components of an input vector that build up via learning in the synaptic matrix with repeated presentations of the input exemplars. These autocorrelation components represent the average correlation between the different elements of the input vector, and this is highest for the central tendency or prototype.		0
Rolls; Memory, Attention, and Decision-Making	565		Recall is very fast on each neuron on a single iteration, because the pattern on the axons can be applied simultaneously to the synapses, and the activation can be accumulated in one or two time constants of the dendrite (e.g. 10-20 ms)		0
Rolls; Memory, Attention, and Decision-Making	565		If the neurons are analyzed and modelled as 'integrate and fire' neurons, then the network can effectively 'relax' into its recall state very rapidly, in one or two time constants of the synapses, corresponding to perhaps 20 ms in the brain.		0
Rolls; Memory, Attention, and Decision-Making	566		One factor in this rapid dynamics of  autoassociative networks is that with some spontaneous activity, some of the neurons in the network are close to threshold already before the recall cue is applied, and hence some of the neurons are very quickly pushed by the recall cue into firing, so that information starts to be exchanged very rapidly (within 1-2 ms through the modified synapses  by the neurons in the network.		1
Rolls; Memory, Attention, and Decision-Making	566		The progressive exchange of information starting early on  within what would otherwise be thought about as an iteration period (of perhaps 20 ms, corresponding to a neuronal firing rate of 50 spikes/s) is the mechanism accounting for rapid recall in an autoassociative neuronal network made biologically realistic in this way		0
Rolls; Memory, Attention, and Decision-Making	566		Networks with recurrent connections, not just autoassociators, can operate much faster than simple models that follow discrete dynamics and is probably a major factor enabling these networks to provide some of the building blocks of brain function.		0
Rolls; Memory, Attention, and Decision-Making	566		Learning is fast, 'one-shot' in that a single presentation of an input pattern enables the association between the activation of the dendrites and the firing of the recurrent collateral axons to be learned.		0
Rolls; Memory, Attention, and Decision-Making	566		Repeated presentation with small variations of the pattern vector is used to obtain the properties of prototype extraction, extraction of central tendency, and noise reduction, because these arise from the averaging process produced by storing very similar patterns in the network.		0
Rolls; Memory, Attention, and Decision-Making	568		The main factors that determine the maximum number of memories that can be stored in an autoassociative network are thus the number of connections on each neuron devoted to the recurrent collaterals, and the sparseness of the representation.		2
Rolls; Memory, Attention, and Decision-Making	568		Increasing the number of neurons (without increasing the number of connections per neuron), does not increase the number of different patterns that can be stored, although it may enable simpler encoding of the firing patterns, for example more orthogonal encoding.		0
Rolls; Memory, Attention, and Decision-Making	568		The environmental context in which learning occurs can be very important factor that affects retrieval in humans and other animals. Placing the subject back into the same context in which the original learning occurred can greatly facilitate retrieval.		0
Rolls; Memory, Attention, and Decision-Making	568		The effects that mood has on memory including visual memory retrieval may be accounted for by backprojections from brain regions such as the amygdala and orbitofrontal cortex in which the current mood, providing a context, is represented, to brain regions involved in memory  such as perirhinal cortex, and in visual representations such as the inferior temporal visual cortex.		0
Rolls; Memory, Attention, and Decision-Making	569		Context effects will be especially important at late stages of memory or information processing systems in the brain, for there information from a wide variety of modalities will be mixed, and some of that information could reflect the context in which the learning takes place.		1
Rolls; Memory, Attention, and Decision-Making	569		Context effects may be strong in the hippocampus, which is implicated in the memory of recent episodes, and which receives inputs derived from most to the cortical information processing streams, including those involved in space.		0
Rolls; Memory, Attention, and Decision-Making	569		Memory for Sequences		0
Rolls; Memory, Attention, and Decision-Making	570		Use of autoassociation networks in the brain		1
Rolls; Memory, Attention, and Decision-Making	571		Competitive networks, including self-organizing maps		1
Rolls; Memory, Attention, and Decision-Making	573		Feature discovery by self-organization		2
Rolls; Memory, Attention, and Decision-Making	573		Removal of redundancy		0
Rolls; Memory, Attention, and Decision-Making	574		Orthogonalization and categorization		1
Rolls; Memory, Attention, and Decision-Making	575		Sparsification		1
Rolls; Memory, Attention, and Decision-Making	577		Comparison to principal component analysis (PCA) and cluster analysis		2
Rolls; Memory, Attention, and Decision-Making	578		Utility of competitive networks in information processing by the brain		1
Rolls; Memory, Attention, and Decision-Making	578		Feature analysis and preprocessing		0
Rolls; Memory, Attention, and Decision-Making	578		Neurons that respond to correlated combinations of their inputs can be described in feature analyzers.		0
Rolls; Memory, Attention, and Decision-Making	578		The power of competitive networks in multistage hierarchical processing builds combinations of what is found at earliest stages, and thus effectively builds higher order representations.		0
Rolls; Memory, Attention, and Decision-Making	578		Competitive networks can learn about individual objects even when multiple objects are presented simultaneously, provided that each object is represented several times more frequently than it is paired with any other individual object.		0
Rolls; Memory, Attention, and Decision-Making	578		Learning in competitive networks is primarily about forming representations of objects defined by a high correlation of coactive features in the input space.		0
Rolls; Memory, Attention, and Decision-Making	578		Removal of redundancy		0
Rolls; Memory, Attention, and Decision-Making	579		Orthogonalization		1
Rolls; Memory, Attention, and Decision-Making	579		Sparsification		0
Rolls; Memory, Attention, and Decision-Making	579		Brain systems in which competitive networks may be used for orthogonalization and sparsification.		0
Rolls; Memory, Attention, and Decision-Making	579		Guidance of competitive learning		0
Rolls; Memory, Attention, and Decision-Making	579		Although competitive networks are primarily unsupervised networks, it is possible to influence the categories found by supplying a second input.		0
Rolls; Memory, Attention, and Decision-Making	582		Topographic map formation		3
Rolls; Memory, Attention, and Decision-Making	586		Invariance learning by competitive networks		4
Rolls; Memory, Attention, and Decision-Making	591		Large competitive nets		5
Rolls; Memory, Attention, and Decision-Making	593		Continuous attractor networks		2
Rolls; Memory, Attention, and Decision-Making	604		Network dynamics: the integrate-and-fire approach		11
Rolls; Memory, Attention, and Decision-Making	610		An integrate-and-fire implementation		6
Rolls; Memory, Attention, and Decision-Making	611		Simulation of fMRI signals: hemodynamic convolution of synaptic activity		1
Rolls; Memory, Attention, and Decision-Making	613		The speed of processing of one-layer attractor networks with integrate-and-fire neurons.		2
Rolls; Memory, Attention, and Decision-Making	659		Is the information that must be stored present in the firing rates or is it present in the synchronized firing of subsets of neurons?		46
Rolls; Memory, Attention, and Decision-Making	659		If the information is present in the firing rates, how much information is available from the spiking activity in a short period, e.g. 20 or 50 ms?		0
Rolls; Memory, Attention, and Decision-Making	659		For each stage of the cortical processing to operate quickly (e.g. 20 ms), it is necessary for each stage to be able to read the code being provided by the previous cortical area within this order of time.		0
Rolls; Memory, Attention, and Decision-Making	659		Understanding the neural code is fundamental to understanding how each stage of the processing works in the brain, and for understanding the speed of processing at each stage.		0
Rolls; Memory, Attention, and Decision-Making	659		We can address the richness of the representation by understanding the differences in the responses of different neurons, and the impact this has on the amount of information that is encoded.		0
Rolls; Memory, Attention, and Decision-Making	659		We must seek understanding at the level of the activity of single neurons and of populations of single neurons, and understanding at this neuronal level (rather than at the level of thousands or millions of neurons as revealed by the functional imaging) is essential for understanding brain computation.		0
Rolls; Memory, Attention, and Decision-Making	659		Information theory provides a means for quantifying how much neurons communicate to other neurons, and thus provide a quantitative approach to fundamental questions about information processing in the brain.		0
Rolls; Memory, Attention, and Decision-Making	659		To investigate the speed of information transmission, one must define and measure information rates from neuronal responses.		0
Rolls; Memory, Attention, and Decision-Making	662		Information sources, information channels, and information  measures.		3
Rolls; Memory, Attention, and Decision-Making	663		The information transmitted by a channel can range from zero to the lower of two independent upper bounds: the entropy of the source, and the capacity of the channel.		1
Rolls; Memory, Attention, and Decision-Making	663		The information carried by neuronal response and its averages.		0
Rolls; Memory, Attention, and Decision-Making	664		Mutual information emphasizes that responses tell us about stimuli just as much as stimuli tell us about responses.		1
Rolls; Memory, Attention, and Decision-Making	666		Information retrieved from an autoassociative memory.		2
Rolls; Memory, Attention, and Decision-Making	668		Estimating the information carried by neuronal responses		2
Rolls; Memory, Attention, and Decision-Making	683		The sparseness out the distributed encoding used by the brain.		15
Rolls; Memory, Attention, and Decision-Making	685		Firing rate distribution of a single neuron in the temporal visual cortex (diagram)		2
Rolls; Memory, Attention, and Decision-Making	688		The peak at low but nonzero rates may be related to the low firing rate spontaneous activity that is typical of many cortical neurons.		3
Rolls; Memory, Attention, and Decision-Making	689		A neuron with inputs from the inferior temporal visual cortex will receive an exponential distribution of firing rates on its afferents.		1
Rolls; Memory, Attention, and Decision-Making	689		At the level of single neurons, an exponential probability density function is consistent with minimizing energy utilization, and maximizing information transmission, for a given mean firing rate.		0
Rolls; Memory, Attention, and Decision-Making	690		Population sparseness		1
Rolls; Memory, Attention, and Decision-Making	691		Population sparseness -- firing rates of a population of inferior temporal cortex neurons to any one stimulus from a set of 20 face and non-face stimuli.  (diagram)		1
Rolls; Memory, Attention, and Decision-Making	692		Comparisons of sparseness between areas -- the hippocampus, insula, orbitofrontal cortex, and amygdala.		1
Rolls; Memory, Attention, and Decision-Making	693		The information from single neurons.		1
Rolls; Memory, Attention, and Decision-Making	697		The information from single neurons -- temporal codes versus rate codes within the spike trains of a single neuron.		4
Rolls; Memory, Attention, and Decision-Making	698		The information from single neurons -- speed of information transfer		1
Rolls; Memory, Attention, and Decision-Making	702		How rapid information can be read from neurons is crucial for understanding how any stage of cortical processing operates, given that each stage includes associative or competitive network processes that require the code to be read before you can pass useful output to the next stage of processing.		4
Rolls; Memory, Attention, and Decision-Making	702		A rapid readout of information from any one stage is important, for the ventral visual system is organized as a hierarchy of cortical areas, and the neuronal response latencies are approximately 100 ms in inferior temporal visual cortex, and 40-50 ms in the primary visual cortex, allowing only 50-60 ms of processing time for V1 -- V2 -- V4 --  inferior temporal cortex.		0
Rolls; Memory, Attention, and Decision-Making	702		There is much evidence that the time required for each stage of processing is relatively short.		0
Rolls; Memory, Attention, and Decision-Making	702		Visual stimuli presented in succession approximately 15 ms apart can be separately identified.		0
Rolls; Memory, Attention, and Decision-Making	703		The number of spikes in a fixed time window over which a postsynaptic neuron could integrate information is a more realistic code.  This time might be of the order of 20 ms for single receiving neuron, or much longer if the receiving neurons are connected by recurrent collateral associative synapses and so can integrate information over time.		1
Rolls; Memory, Attention, and Decision-Making	703		Although the number of spikes in a short time window, e.g. 20 ms is likely to be 0, 1, or 2, the information available may be more than that from the first spike alone.		0
Rolls; Memory, Attention, and Decision-Making	705		Speed of information availability in the inferior temporal visual cortex. Cumulative single cell information. (diagram)		2
Rolls; Memory, Attention, and Decision-Making	705		An attractor network might be able to integrate the information arriving over a long time period of several hundred milliseconds.		0
Rolls; Memory, Attention, and Decision-Making	705		A single-layer pattern association network might only be able to integrate the information over the time constants of the synapses and cell membrane, which might be of the order of 15-30 ms.		0
Rolls; Memory, Attention, and Decision-Making	705		In a hierarchical processing system such as the visual cortical areas, there may only be a short time during which each stage may decode the information from the preceding stage, and then pass on information sufficient to support recognition to the next stage.		0
Rolls; Memory, Attention, and Decision-Making	706		Speed of information availability in the inferior temporal visual cortex. Cumulative multiple cell information. (diagram)		1
Rolls; Memory, Attention, and Decision-Making	706		With a population of neurons, having just one spike from each neuron can allow considerable information to be read if only a limited (e.g. 20-50 ms) is available for the readout.		0
Rolls; Memory, Attention, and Decision-Making	709		The information from multiple cells -- independent information versus redundancy across cells.		3
Rolls; Memory, Attention, and Decision-Making	715		The information from multiple cells -- the effects of cross correlations between cells.		6
Rolls; Memory, Attention, and Decision-Making	718		Research results are consistent with the hypothesis that feature binding is implemented by neurons that respond to features in the correct relative spatial locations, and not by temporal synchrony and attention.		3
Rolls; Memory, Attention, and Decision-Making	718		Even if stimulus dependent synchrony was useful for grouping, it would not without much extra machinery be useful for binding the relatives spatial positions of features within an object, or for that matter of the position of objects in a scene, which appear to be encoded in a different way.		0
Rolls; Memory, Attention, and Decision-Making