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E T Rolls

Publications and source records attributed to E T Rolls.

At least 37 records · Page 2Linked to original sources
PubMed2001

Role of long-term synaptic modification in short-term memory.

One way that some types of short-term or working memory may be implemented in the brain is by using autoassociation networks that recirculate information to maintain the firing of a subset of neurons in what is termed an attractor state. We describe how long-term synaptic modification is necessary to set up the appropriate stable attractors, each one of which corresponds to a memory of a particular item. Once the synapses have been modified, any of the short-term memory states may be triggered by an appropriate input which starts the neurons firing in one of the attractors, and then the firing is maintained in that attractor by the already modified synapses, with no further synaptic modification necessary. This analysis leads to the prediction that if this type of implementation is used for working memory, then long-term synaptic modification may be necessary only during an acquisition phase of a task, and once the task has been acquired, the performance of the working memory task should be unimpaired if no further synaptic modification is allowed. We show that a considerable body of research findings on the effects of agents that block synaptic modification on working memory tasks can be understood in this way. Many of the findings are consistent with the hypothesis that blocking synaptic modification in the hippocampus impairs the acquisition, but not the later performance, of hippocampal-dependent working memory tasks.

Animals↗
PubMed2001

A view model which accounts for the spatial fields of hippocampal primate spatial view cells and rat place cells.

Hippocampal spatial view cells found in primates respond to a region of visual space being looked at, relatively independently of where the monkey is located. Rat place cells have responses which depend on where the rat is located. We investigate the hypothesis that in both types of animal, hippocampal cells respond to a combination of visual cues in the correct spatial relation to each other. In rats, which have a wide visual field, such a combination might define a place. In primates, including humans, which have a much smaller visual field and a fovea which is directed towards a part of the environment, the same mechanism might lead to spatial view cells. A computational model in which the neurons become organized by learning to respond to a combination of a small number of visual cues spread within an angle of a 30 degrees receptive field resulted in cells with visual properties like those of primate spatial view cells. The same model, but operating with a receptive field of 270 degrees, produced cells with visual properties like those of rat place cells. Thus a common hippocampal mechanism operating with different visual receptive field sizes could account for some of the visual properties of both place cells in rodents and spatial view cells in primates.

Animals↗
PubMed2001

Abstract reward and punishment representations in the human orbitofrontal cortex.

The orbitofrontal cortex (OFC) is implicated in emotion and emotion-related learning. Using event-related functional magnetic resonance imaging (fMRI), we measured brain activation in human subjects doing an emotion-related visual reversal-learning task in which choice of the correct stimulus led to a probabilistically determined 'monetary' reward and choice of the incorrect stimulus led to a monetary loss. Distinct areas of the OFC were activated by monetary rewards and punishments. Moreover, in these areas, we found a correlation between the magnitude of the brain activation and the magnitude of the rewards and punishments received. These findings indicate that one emotional involvement of the human orbitofrontal cortex is its representation of the magnitudes of abstract rewards and punishments, such as receiving or losing money.

Adult↗
PubMed2001

The rules of formation of the olfactory representations found in the orbitofrontal cortex olfactory areas in primates.

Approximately 35% of neurons in the orbitofrontal cortex taste and olfactory areas with olfactory responses provide a representation of odour that depends on the taste with which the odour has been associated previously. This representation is produced by a slowly acting learning mechanism that learns associations between odour and taste. Other neurons in the orbitofrontal cortex respond to both the odour and to the mouth feel of fat. The representation of odour thus moves for at least some neurons in the orbitofrontal cortex beyond the domain of physico-chemical properties of the odours to a domain where the ingestion-related significance of the odour determines the representation provided. Olfactory neurons in the primate orbitofrontal cortex decrease their responses to a food eaten to satiety, but remain responsive to other foods, thus contributing to a mechanism for olfactory sensory-specific satiety. It has been shown in neuroimaging studies that the human orbitofrontal cortex provides a representation of the pleasantness of odour, in that the activation produced by the odour of a food eaten to satiety decreases relative to another food-related odour not eaten in the meal. In the same general area there is a representation of the pleasantness of the smell, taste and texture of a whole food, in that activation in this area decreases to a food eaten to satiety, but not to a food that has not been eaten in the meal.

Animals↗
PubMed2001

Representation of pleasant and aversive taste in the human brain.

In this study, the representation of taste in the orbitofrontal cortex was investigated to determine whether or not a pleasant and an aversive taste have distinct or overlapping representations in this region. The pleasant stimulus used was sweet taste (1 M glucose), and the unpleasant stimulus was salt taste (0.1 M NaCl). We used an ON/OFF block design in a 3T fMRI scanner with a tasteless solution delivered in the OFF period to control for somatosensory or swallowing-related effects. It was found that parts of the orbitofrontal cortex were activated (P < 0.005 corrected) by glucose (in 6/7 subjects) and by salt (in 6/7 subjects). In the group analysis, separate areas of the orbitofrontal cortex were found to be activated by pleasant and aversive tastes. The involvement of the amygdala in the representation of pleasant as well as aversive tastes was also investigated. The amygdala was activated (region of interest analysis, P < 0.025 corrected) by the pleasant taste of glucose (5/7 subjects) as well as by the aversive taste of salt (4/7 subjects). Activation by both stimuli was also found in the frontal opercular/insular (primary) taste cortex. We conclude that the orbitofrontal cortex is involved in processing tastes that have both positive and negative affective valence and that different areas of the orbitofrontal cortex may be activated by pleasant and unpleasant tastes. We also conclude that the amygdala is activated not only by an affectively unpleasant taste, but also by a taste that is affectively pleasant, thus providing evidence that the amygdala is involved in effects produced by positively affective as well as by negatively affective stimuli.

Amygdala↗
PubMed2001

Invariant object recognition in the visual system with error correction and temporal difference learning.

It has been proposed that invariant pattern recognition might be implemented using a learning rule that utilizes a trace of previous neural activity which, given the spatio-temporal continuity of the statistics of sensory input, is likely to be about the same object though with differing transforms in the short time scale. Recently, it has been demonstrated that a modified Hebbian rule which incorporates a trace of previous activity but no contribution from the current activity can offer substantially improved performance. In this paper we show how this rule can be related to error correction rules, and explore a number of error correction rules that can be applied to and can produce good invariant pattern recognition. An explicit relationship to temporal difference learning is then demonstrated, and from this further learning rules related to temporal difference learning are developed. This relationship to temporal difference learning allows us to begin to exploit established analyses of temporal difference learning to provide a theoretical framework for better understanding the operation and convergence properties of these learning rules, and more generally, of rules useful for learning invariant representations. The efficacy of these different rules for invariant object recognition is compared using VisNet, a hierarchical competitive network model of the operation of the visual system.

Learning↗
PubMed2001

A model of the interaction between mood and memory.

This paper investigates a neural network model of the interaction between mood and memory. The model has two attractor networks that represent the inferior temporal cortex (IT), which stores representations of visual stimuli, and the amygdala, the activity of which reflects the mood state. The two attractor networks are coupled by forward and backward projections. The model is however generic, and is relevant to understanding the interaction between different pairs of modules in the brain, particularly, as is the case with moods and memories, when there are fewer states represented in one module than in the other. During learning, a large number of patterns are presented to the IT, each paired with one of two mood states represented in the amygdala. The recurrent connections within each module, the forward connections from the memory module to the amygdala, and the backward connections from the amygdala to the memory module, are associatively modified. It is shown how the mood state in the amygdala can influence which memory patterns are recalled in the memory module. Further, it is shown that if there is an existing mood state in the amygdala, it can be difficult to change it even when a retrieval cue is presented to the memory module that is associated with a different mood state. It is also shown that the backprojections from the amygdala to the memory module must be relatively weak if memory retrieval in the memory module is not to be disrupted. The results are relevant to understanding the interaction between structures important in mood and emotion (such as the amygdala and orbitofrontal cortex) and other brain areas involved in storing objects and faces (such as the inferior temporal visual cortex) and memories (such as the hippocampus).

Affect↗
PubMed2001

Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons.

The speed of processing in the visual cortical areas can be fast, with for example the latency of neuronal responses increasing by only approximately 10 ms per area in the ventral visual system sequence V1 to V2 to V4 to inferior temporal visual cortex. This has led to the suggestion that rapid visual processing can only be based on the feedforward connections between cortical areas. To test this idea, we investigated the dynamics of information retrieval in multiple layer networks using a four-stage feedforward network modelled with continuous dynamics with integrate-and-fire neurons, and associative synaptic connections between stages with a synaptic time constant of 10 ms. Through the implementation of continuous dynamics, we found latency differences in information retrieval of only 5 ms per layer when local excitation was absent and processing was purely feedforward. However, information latency differences increased significantly when non-associative local excitation was included. We also found that local recurrent excitation through associatively modified synapses can contribute significantly to processing in as little as 15 ms per layer, including the feedforward and local feedback processing. Moreover, and in contrast to purely feed-forward processing, the contribution of local recurrent feedback was useful and approximately this rapid even when retrieval was made difficult by noise. These findings suggest that cortical information processing can benefit from recurrent circuits when the allowed processing time per cortical area is at least 15 ms long.

Action Potentials↗
PubMed2000

Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex.

When a food is eaten to satiety, its reward value decreases. This decrease is usually greater for the food eaten to satiety than for other foods, an effect termed sensory-specific satiety. In an fMRI investigation it was shown that for a region of the orbitofrontal cortex the activation produced by the odour of the food eaten to satiety decreased, whereas there was no similar decrease for the odour of a food not eaten in the meal. This effect was shown both by a voxel-wise SPM contrast (p <0.05 corrected) and an ANOVA performed on the mean percentage change in BOLD signal in the identified clusters of voxels (p <0.006). These results show that activation of a region of the human orbitofrontal cortex is related to olfactory sensory-specific satiety.

Eating↗
PubMed2000

Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex.

When a food is eaten to satiety, its reward value decreases. This decrease is usually greater for the food eaten to satiety than for other foods, an effect termed sensory-specific satiety. In an fMRI investigation it was shown that for a region of the orbitofrontal cortex the activation produced by the odour of the food eaten to satiety decreased, whereas there was no similar decrease for the odour of a food not eaten in the meal. This effect was shown both by a voxel-wise SPM contrast (p<0.05 corrected) and an ANOVA performed on the mean percentage change in BOLD signal in the identified clusters of voxels (p<0.006). These results show that activation of a region of the human orbitofrontal cortex is related to olfactory sensory-specific satiety.

Brain Mapping↗
PubMed2000

Hippocampo-cortical and cortico-cortical backprojections.

First, the information represented in the primate hippocampus, and what is computed by the primate hippocampus, are considered. Then a theory is described of how the information represented in the hippocampus is able to influence the cerebral cortex by a hierarchy of hippocampo-cortical and cortico-cortical backprojection stages. The recalled backprojected information in the cerebral neocortex could then be used by the neocortex as part of memory recall, including that required in spatial working memory; to influence the processing that each cortical stage performs based on its forward inputs; to influence the formation of long-term memories; and/or in the selection of appropriate actions.

Animals↗
PubMed2000

On the design of neural networks in the brain by genetic evolution.

Hypotheses are presented of what could be specified by genes to enable the different functional architectures of the neural networks found in the brain to be built during ontogenesis. It is suggested that for each class of neuron (e.g., hippocampal CA3 pyramidal cells) a small number of genes specify the generic properties of that neuron class (e.g., the number of neurons in the class, and the firing threshold), while a larger number of genes specify the properties of the synapses onto that class of neuron from each of the other classes that makes synapses with it. These properties include not only which other neuron classes the synapses come from, but whether they are excitatory or inhibitory, the nature of the learning rule implemented at the synapse, and the initial strength of such synapses. In a demonstration of the feasibility of the hypotheses to specify the architecture of different types of neuronal network, a genetic algorithm is used to allow the evolution of genotypes which are capable of specifying neural networks that can learn to solve particular computational tasks, including pattern association, autoassociation, and competitive learning. This overall approach allows such hypotheses to be further tested, improved, and extended with the help of neuronal network simulations with genetically specified architectures in order to develop further our understanding of how the architecture and operation of different parts of brains are specified by genes, and how different parts of our brains have evolved to perform particular functions.

Algorithms↗
PubMed2000

Position invariant recognition in the visual system with cluttered environments.

The effects of cluttered environments are investigated on the performance of a hierarchical multilayer model of invariant object recognition in the visual system (VisNet) that employs learning rules that utilise a trace of previous neural activity. This class of model relies on the spatio-temporal statistics of natural visual inputs to be able to associate together different exemplars of the same stimulus or object which will tend to occur in temporal proximity. In this paper the different exemplars of a stimulus are the same stimulus in different positions. First it is shown that if the stimuli have been learned previously against a plain background, then the stimuli can be correctly recognised even in environments with cluttered (e.g. natural) backgrounds which form complex scenes. Second it is shown that the functional architecture has difficulty in learning new objects if they are presented against cluttered backgrounds. It is suggested that processes such as the use of a high-resolution fovea, or attention, may be particularly useful in suppressing the effects of background noise and in segmenting objects from their background when new objects need to be learned. However, it is shown third that this problem may be ameliorated by the prior existence of stimulus tuned feature detecting neurons in the early layers of the VisNet, and that these feature detecting neurons may be set up through previous exposure to the relevant class of objects. Fourth we extend these results to partially occluded objects, showing that (in contrast with many artificial vision systems) correct recognition in this class of architecture can occur if the objects have been learned previously without occlusion.

Animals↗
PubMed2000

A recurrent model of transformation invariance by association.

This paper describes an investigation of a recurrent artificial neural network which uses association to build transform-invariant representations. The simulation implements the analytic model of Parga and Rolls [(1998). Transform-invariant recognition by association in a recurrent network. Neural Computation 10(6), 1507-1525.] which defines multiple (e.g. "view") patterns to be within the basin of attraction of a shared (e.g. "object") representation. First, it was shown that the network could store and correctly retrieve an "object" representation from any one of the views which define that object, with capacity as predicted analytically. Second, new results extended the analysis by showing that correct object retrieval could occur where retrieval cues were distorted; where there was some association between the views of different objects; and where connectivity was diluted, even when this dilution was asymmetric. The simulations also extended the analysis by showing that the system could work well with sparse patterns; and showing how pattern sparseness interacts with the number of views of each object (as a result of the statistical properties of the pattern coding) to give predictable object retrieval performance. The results thus usefully extend a recurrent model of invariant pattern recognition.

Artificial Intelligence↗
PubMed2000

Précis of The brain and emotion.

The topics treated in The brain and emotion include the definition, nature, and functions of emotion (Ch. 3); the neural bases of emotion (Ch. 4); reward, punishment, and emotion in brain design (Ch. 10); a theory of consciousness and its application to understanding emotion and pleasure (Ch. 9); and neural networks and emotion-related learning (Appendix). The approach is that emotions can be considered as states elicited by reinforcers (rewards and punishers). This approach helps with understanding the functions of emotion, with classifying different emotions, and in understanding what information-processing systems in the brain are involved in emotion, and how they are involved. The hypothesis is developed that brains are designed around reward- and punishment-evaluation systems, because this is the way that genes can build a complex system that will produce appropriate but flexible behavior to increase fitness (Ch. 10). By specifying goals rather than particular behavioral patterns of responses, genes leave much more open the possible behavioral strategies that might be required to increase fitness. The importance of reward and punishment systems in brain design also provides a basis for understanding the brain mechanisms of motivation, as described in Chapters 2 for appetite and feeding, 5 for brain-stimulation reward, 6 for addiction, 7 for thirst, and 8 for sexual behavior.

Affect↗
PubMed2000

The orbitofrontal cortex and reward.

The primate orbitofrontal cortex contains the secondary taste cortex, in which the reward value of taste is represented. It also contains the secondary and tertiary olfactory cortical areas, in which information about the identity and also about the reward value of odors is represented. The orbitofrontal cortex also receives information about the sight of objects and faces from the temporal lobe cortical visual areas, and neurons in it learn and reverse the visual stimulus to which they respond when the association of the visual stimulus with a primary reinforcing stimulus (such as a taste reward) is reversed. However, the orbitofrontal cortex is involved in representing negative reinforcers (punishers) too, such as aversive taste, and in rapid stimulus-reinforcement association learning for both positive and negative primary reinforcers. In complementary neuroimaging studies in humans it is being found that areas of the orbitofrontal cortex (and connected subgenual cingulate cortex) are activated by pleasant touch, by painful touch, by rewarding and aversive taste, and by odor. Damage to the orbitofrontal cortex in humans can impair the learning and reversal of stimulus- reinforcement associations, and thus the correction of behavioral responses when these are no longer appropriate because previous reinforcement contingencies change. This evidence thus shows that the orbitofrontal cortex is involved in decoding and representing some primary reinforcers such as taste and touch; in learning and reversing associations of visual and other stimuli to these primary reinforcers; and in controlling and correcting reward-related and punishment-related behavior, and thus in emotion.

Afferent Pathways↗
PubMed2000

The representation of umami taste in the taste cortex.

To investigate the neural encoding of glutamate (umami) taste in the primate, recordings were made from taste-responsive neurons in the cortical taste areas in macaques. Most of the neurons were in the orbitofrontal cortex (secondary) taste area. First, it was shown that there is a representation of the taste of glutamate that is separate from the representation of the other prototypical tastants, sweet (glucose), salt (NaCl), bitter (quinine) and sour (HCl). Second, it was shown that single neurons that had their best responses to sodium glutamate also had good responses to glutamic acid. Third, it was shown that the responses of these neurons to the nucleotide umami tastant inosine 5'-monophosphate were more correlated with their responses to monosodium glutamate than to any prototypical tastant. Fourth, concentration-response curves showed that concentrations of monosodium glutamate as low as 0.001 mol/L were just above threshold for some of these neurons. Fifth, some neurons in the orbitofrontal region which responded to monosodium glutamate and other food tastes, decreased their responses after feeding with monosodium glutamate to behavioral satiety, revealing a mechanism of satiety. In some cases, this reduction was sensory-specific. Sixth, it was shown in psychophysical experiments in humans that the flavor of umami is strongest with a combination of corresponding taste and olfactory stimuli (e.g., monosodium glutamate and garlic odor). The hypothesis is proposed that part of the way in which glutamate works as a flavor enhancer is by acting in combination with corresponding food odors. The appropriate associations between the odor and the glutamate taste may be learned at least in part by olfactory to taste association learning in the primate orbitofrontal cortex.

Animals↗