Biomedical subjects

S M Stringer

Publications and source records attributed to S M Stringer.

17 recordsLinked to original sources

PubMed2006

Learning movement sequences with a delayed reward signal in a hierarchical model of motor function.

A key problem in reinforcement learning is how an animal is able to learn a sequence of movements when the reward signal only occurs at the end of the sequence. We describe how a hierarchical dynamical model of motor function is able to solve the problem of delayed reward in learning movement sequences using associative (Hebbian) learning. At the lowest level, the motor system encodes simple movements or primitives, while at higher levels the system encodes sequences of primitives. During training, the network is able to learn a high level motor program composed of a specific temporal sequence of motor primitives. The network is able to achieve this despite the fact that the reward signal, which indicates whether or not the desired motor program has been performed correctly, is received only at the end of each trial during learning. Use of a continuous attractor network in the architecture enables the network to generate the motor outputs required to produce the continuous movements necessary to implement the motor sequence.

Animals↗

PubMed2005

Learning invariant object recognition in the visual system with continuous transformations.

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm "continuous transformation learning" which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.

Computer Simulation↗

PubMed2005

Self-organizing continuous attractor network models of hippocampal spatial view cells.

Single neuron recording studies have demonstrated the existence of hippocampal spatial view neurons which encode information about the spatial location at which a primate is looking in the environment. These neurons are able to maintain their firing even in the absence of visual input. The standard neuronal network approach to model networks with memory that represent continuous spaces is that of continuous attractor networks. It has recently been shown how idiothetic (self-motion) inputs could update the activity packet of neuronal firing for a one-dimensional case (head direction cells), and for a two-dimensional case (place cells which represent the place where a rat is located). In this paper, we describe three models of primate hippocampal spatial view cells, which not only maintain their spatial firing in the absence of visual input, but can also be updated in the dark by idiothetic input. The three models presented in this paper represent different ways in which a continuous attractor network could integrate a number of different kinds of velocity signal (e.g., head rotation and eye movement) simultaneously. The first two models use velocity information from head angular velocity and from eye velocity cells, and make use of a continuous attractor network to integrate this information. A fundamental feature of the first two models is their use of a 'memory trace' learning rule which incorporates a form of temporal average of recent cell activity. Rules of this type are able to build associations between different patterns of neural activities that tend to occur in temporal proximity, and are incorporated in the model to enable the recent change in the continuous attractor to be associated with the contemporaneous idiothetic input. The third model uses positional information from head direction cells and eye position cells to update the representation of where the agent is looking in the dark. In this case the integration of idiothetic velocity signals is performed in the earlier layer of head direction cells.

Action Potentials↗

PubMed2004

Self-organising continuous attractor networks with multiple activity packets, and the representation of space.

'Continuous attractor' neural networks can maintain a localised packet of neuronal activity representing the current state of an agent in a continuous space without external sensory input. In applications such as the representation of head direction or location in the environment, only one packet of activity is needed. For some spatial computations a number of different locations, each with its own features, must be held in memory. We extend previous approaches to continuous attractor networks (in which one packet of activity is maintained active) by showing that a single continuous attractor network can maintain multiple packets of activity simultaneously, if each packet is in a different state space or map. We also show how such a network could by learning self-organise to enable the packets in each space to be moved continuously in that space by idiothetic (motion) inputs. We show how such multi-packet continuous attractor networks could be used to maintain different types of feature (such as form vs colour) simultaneously active in the correct location in a spatial representation. We also show how high-order synapses can improve the performance of these networks, and how the location of a packet could be read by motor networks. The multiple packet continuous attractor networks described here may be used for spatial representations in brain areas such as the parietal cortex and hippocampus.

Brain↗

PubMed2003

Self-organizing continuous attractor networks and motor function.

Motor skill learning may involve training a neural system to automatically perform sequences of movements, with the training signals provided by a different system, used mainly during training to perform the movements, that operates under visual sensory guidance. We use a dynamical systems perspective to show how complex motor sequences could be learned by the automatic system. The network uses a continuous attractor network architecture to perform path integration on an efference copy of the motor signal to keep track of the current state, and selection of which motor cells to activate by a movement selector input where the selection depends on the current state being represented in the continuous attractor network. After training, the correct motor sequence may be selected automatically by a single movement selection signal. A feature of the model presented is the use of 'trace' learning rules which incorporate a form of temporal average of recent cell activity. This form of temporal learning underlies the ability of the networks to learn temporal sequences of behaviour. We show that the continuous attractor network models developed here are able to demonstrate the key features of motor function. That is, (i) the movement can occur at arbitrary speeds; (ii) the movement can occur with arbitrary force; (iii) the agent spends the same relative proportions of its time in each part of the motor sequence; (iv) the agent applies the same relative force in each part of the motor sequence; and (v) the actions always occur in the same sequence.

Excitatory Postsynaptic Potentials↗

PubMed2002

Invariant recognition of feature combinations in the visual system.

The operation of a hierarchical competitive network model (VisNet) of invariance learning in the visual system is investigated to determine how this class of architecture can solve problems that require the spatial binding of features. First, we show that VisNet neurons can be trained to provide transform-invariant discriminative responses to stimuli which are composed of the same basic alphabet of features, where no single stimulus contains a unique feature not shared by any other stimulus. The investigation shows that the network can discriminate stimuli consisting of sets of features which are subsets or supersets of each other. Second, a key feature-binding issue we address is how invariant representations of low-order combinations of features in the early layers of the visual system are able to uniquely specify the correct spatial arrangement of features in the overall stimulus and ensure correct stimulus identification in the output layer. We show that output layer neurons can learn new stimuli if the lower layers are trained solely through exposure to simpler feature combinations from which the new stimuli are composed. Moreover, we show that after training on the low-order feature combinations which are common to many objects, this architecture can--after training with a whole stimulus in some locations--generalise correctly to the same stimulus when it is shown in a new location. We conclude that this type of hierarchical model can solve feature-binding problems to produce correct invariant identification of whole stimuli.

Animals↗

PubMed2002

Self-organizing continuous attractor networks and path integration: one-dimensional models of head direction cells.

Some neurons encode information about the orientation or position of an animal, and can maintain their response properties in the absence of visual input. Examples include head direction cells in rats and primates, place cells in rats and spatial view cells in primates. 'Continuous attractor' neural networks model these continuous physical spaces by using recurrent collateral connections between the neurons which reflect the distance between the neurons in the state space (e.g. head direction space) of the animal. These networks maintain a localized packet of neuronal activity representing the current state of the animal. We show how the synaptic connections in a one-dimensional continuous attractor network (of for example head direction cells) could be self-organized by associative learning. We also show how the activity packet could be moved from one location to another by idiothetic (self-motion) inputs, for example vestibular or proprioceptive, and how the synaptic connections could self-organize to implement this. The models described use 'trace' associative synaptic learning rules that utilize a form of temporal average of recent cell activity to associate the firing of rotation cells with the recent change in the representation of the head direction in the continuous attractor. We also show how a nonlinear neuronal activation function that could be implemented by NMDA receptors could contribute to the stability of the activity packet that represents the current state of the animal.

Animals↗

PubMed2002

Self-organizing continuous attractor networks and path integration: two-dimensional models of place cells.

Single-neuron recording studies have demonstrated the existence of neurons in the hippocampus which appear to encode information about the place where a rat is located, and about the place at which a macaque is looking. We describe 'continuous attractor' neural network models of place cells with Gaussian spatial fields in which the recurrent collateral synaptic connections between the neurons reflect the distance between two places. The networks maintain a localized packet of neuronal activity that represents the place where the animal is located. We show for two related models how the representation of the two-dimensional space in the continuous attractor network of place cells could self-organize by modifying the synaptic connections between the neurons, and also how the place being represented can be updated by idiothetic (self-motion) signals in a neural implementation of path integration.

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

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↗

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↗

PubMed1999

Population dynamics of scrapie in a sheep flock.

A detailed analysis of an outbreak of natural scrapie in a flock of Cheviot sheep is described. A total of 137 cases was reported over 13 years among 1307 sheep born into the flock. The epidemiology of scrapie can only be understood with reference to sheep demography, the population genetics of susceptibility to scrapie, pathogenesis during a long incubation period, and the rate of transmission (by both vertical and horizontal routes), all of which interact in complex ways. A mathematical model incorporating these features is described, parameter values and model inputs are derived from available information from the flock and from independent sources, and model outputs are compared with the field data. The model is able to reproduce key features of the outbreak, including its long duration and the ages of cases. The analysis supports earlier work suggesting that many infected sheep do not survive to show clinical signs, that most cases arise through horizontal transmission, and that there is strong selection against susceptible genotypes. However, important aspects of scrapie epidemiology remain poorly understood, including the possible role of carrier genotypes and of an environmental reservoir of infectivity, and the mechanisms maintaining alleles giving susceptibility to scrapie in the sheep population.

Alleles↗

PubMed1998

Epidemiology and control of scrapie within a sheep flock.

Mathematical models of the transmission dynamics of scrapie are used to explore the expected course of an outbreak in a sheep flock, and the potential impacts of different control measures. All models incorporate sheep demography, a long and variable scrapie incubation period, horizontal and vertical routes of transmission and genetic variation in susceptibility. Outputs are compared for models which do and do not incorporate an environmental reservoir of infectivity, and which do and do not incorporate carrier genotypes. Numerical analyses using parameter values consistent with available data indicate that, in a closed flock, scrapie outbreaks may have a duration of several decades, reduce the frequency of susceptible genotypes, and may become endemic if carrier genotypes are present. In an open flock, endemic scrapie is possible even in the absence of carriers. Control measures currently or likely to become available may reduce the incidence of cases but may be fully effective only over a period of several years.

Animals↗

PubMed1998

A mathematical model of the dynamics of scrapie in a sheep flock.

A mathematical model is developed for the dynamics of an outbreak of scrapie in a single sheep flock with the aim of assisting the interpretation of field data. The model incorporates age structure of the sheep population, both horizontal and vertical transmission, genetic predisposition to infection, variable initial load of the infectious agent, and increasing infection load during an incubation period of the same order as sheep life expectancy. This leads to system of partial differential equations with respect to time, age and infection load. Numerical analyses using this model demonstrate that a scrapie outbreak is likely to be of long duration (several decades), will lead to reduction of scrapie susceptible allele frequency (but not to zero), and has different dynamics in homozygous and heterozygous susceptible sheep, even if these genotypes are equally susceptible, due to the different contributions of vertical infection to transmission to genotypes.

Age Factors↗

PubMedDate not supplied

The perirhinal cortex and long-term familiarity memory.

To analyse the functions of the perirhinal cortex, the activity of single neurons in the perirhinal cortex was recorded while macaques performed a delayed matching-to-sample task with up to three intervening stimuli. Some neurons had activity related to working memory, in that they responded more to the sample than to the match image within a trial, as shown previously. However, when a novel set of stimuli was introduced, the neuronal responses were on average only 47% of the magnitude of the responses to the set of very familiar stimuli. Moreover, it was shown in three monkeys that the responses of the perirhinal cortex neurons gradually increased over hundreds of presentations (mean = 400 over 7-13 days) of the new set of (initially novel) stimuli to become as large as those to the already familiar stimuli. Thus perirhinal cortex neurons represent the very long-term familiarity of visual stimuli. Part of the impairment in temporal lobe amnesia may be related to the difficulty of building representations of the degree of familiarity of stimuli. A neural network model of how the perirhinal cortex could implement long-term familiarity memory is proposed using Hebbian associative learning.

Animals↗