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L N Cooper

Publications and source records attributed to L N Cooper.

17 recordsLinked to original sources
PubMed1998

The effect of dynamic synapses on spatiotemporal receptive fields in visual cortex.

Temporal dynamics are a general feature of synaptic transmission. Recently, novel aspects of temporal dynamics of synaptic transmission have been reported in the neocortex. Here, we examine the possible effects of these dynamics on the spatiotemporal receptive fields of simple cells in V1. We do this by examining a simple model of a cortical neuron that displays stimulus orientation selectivity as a consequence of the pattern of thalamocortical synaptic weights. In our model, the receptive field structure is encoded functionally in either presynaptic probability of release or postsynaptic efficacy. We show that these different assumptions about the origin of receptive field structure lead to very different spatiotemporal dynamics in the case of flashed-bar stimulus. In addition, the results of the reverse correlation study suggest a possible test for differentiating between models. We also show that the temporal code induced by dynamic synapses can be used to distinguish between different inputs that induce the same average firing rate.

Animals↗
PubMed1997

BCM network develops orientation selectivity and ocular dominance in natural scene environment.

A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation-selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model Shouval et al., 1996.

Adaptation, Ocular↗
PubMed1996

Effect of binocular cortical misalignment on ocular dominance and orientation selectivity.

We model a two-eye visual environment composed of natural images and study its effect on single cell synaptic modification. In particular, we study the effect of binocular cortical misalignment on receptive field formation after eye opening. We show that binocular misalignment affects principal component analysis (PCA) and Bienenstock, Cooper, and Munro (BCM) learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case.

Learning↗
PubMed1996

Organization of receptive fields in networks with Hebbian learning: the connection between synaptic and phenomenological models.

In this paper we address the question of how interactions affect the formation and organization of receptive fields in a network composed of interacting neurons with Hebbian-type learning. We show how to partially decouple single cell effects from network effects, and how some phenomenological models can be seen as approximations to these learning networks. We show that the interaction affects the structure of receptive fields. We also demonstrate how the organization of different receptive fields across the cortex is influenced by the interaction term, and that the type of singularities depends on the symmetries of the receptive fields.

Learning↗
PubMed1994

A model of prenatal acquisition of speech parameters.

An unsupervised neural network model inductively acquires the ability to distinguish categorically the stop consonants of English, in a manner consistent with prenatal and early postnatal auditory experience, and without reference to any specialized knowledge of linguistic structure or the properties of speech. This argues against the common assumption that linguistic knowledge, and speech perception in particular, cannot be learned and must therefore be innately specified.

Fetus↗
PubMed1994

Formation of receptive fields in realistic visual environments according to the Bienenstock, Cooper, and Munro (BCM) theory.

The Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity has successfully reproduced the development of orientation selectivity and ocular dominance in kitten visual cortex in normal, as well as deprived, visual environments. To better compare the consequences of this theory with experiment, previous abstractions of the visual environment are replaced in this work by real visual images with retinal processing. The visual environment is represented by 24 gray-scale natural images that are shifted across retinal fields. In this environment, the BCM neuron develops receptive fields similar to the fields of simple cells found in kitten striate cortex. These fields display adjacent excitatory and inhibitory bands when tested with spot stimuli, orientation selectivity when tested with bar stimuli, and spatial-frequency selectivity when tested with sinusoidal gratings. In addition, their development in various deprived visual environments agrees with experimental results.

Animals↗
PubMed1991

Synaptic plasticity in visual cortex: comparison of theory with experiment.

1. The aim of this work was to assess whether a form of synaptic modification based on the theory of Bienenstock, Cooper, and Munro (BCM) can, with a fixed set of parameters, reproduce both the kinetics and equilibrium states of experience-dependent modifications cortex. 2. According to the BCM theory, the connection strength of excitatory geniculocortical synapses varies as the product of a measure of input activity (d) and a function (phi) of the summed postsynaptic response. For all postsynaptic responses greater than spontaneous but less than a critical value called the "modification threshold" (theta), phi has a negative value. For all postsynaptic responses greater than theta, phi has a positive value. A novel feature of the BCM theory is that the value of theta is not fixed, but rather "slides" as a nonlinear function of the average postsynaptic response. 3. This theory permits precise specification of theoretical equivalents of experimental situations, allowing detailed, quantitative comparisons of theory with experiment. Such comparisons were carried out here in a series of computer simulations. 4. Simulations are performed by presenting input to a model cortical neuron, calculating the summed postsynaptic response, and then changing the synaptic weights according to the BCM theory. This process is repeated until the synaptic weights reach an equilibrium state. 5. Two types of geniculocortical input are simulated: "pattern" and "noise." Pattern input is assumed to correspond to the type of input that arises when a visual contour of a particular orientation is presented to the retina. This type of input is said to be "correlated" when the two sets of geniculocortical fibers relaying information from the two eyes convey the same patterns at the same time. Noise input is assumed to correspond to the type of input that arises in the absence of visual contours and, by definition, is uncorrelated. 6. By varying the types of input available to the two sets of geniculocortical synapses, we simulate the following types of visual experience: 1) normal binocular contour vision, 2) monocular deprivation, 3) reverse suture, 4) strabismus, 5) binocular deprivation, and 6) normal contour vision after a period of monocular deprivation. 7. The constraints placed on the set of parameters by each type of simulated visual environment, and the effects that such constraints have on the evolution of the synaptic weights, are investigated in detail.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1988

Mean-field theory of a neural network.

A single-cell theory for the development of selectivity and ocular dominance in visual cortex has been generalized to incorporate more realistic neural networks that approximate the actual anatomy of small regions of cortex. In particular, we have analyzed a network consisting of excitatory and inhibitory cells, both of which may receive information from the lateral geniculate nucleus (LGN) and then interact through cortico-cortical synapses in a mean-field approximation. Our investigation of the evolution of a cell in this mean-field network indicates that many of the results on existence and stability of fixed points that have been obtained previously in the single-cell theory can be successfully generalized here. We can, in addition, make explicit further statements concerning the independent effects of excitatory and inhibitory neurons on selectivity and ocular dominance. For example, shutting off inhibitory cells lessens selectivity and alters ocular dominance (masked synapses). These inhibitory cells may be selective, but there is no theoretical necessity that they be so. Further, the intercortical inhibitory synapses do not have to be very responsive to visual experience. Most of the learning process can occur among the excitatory LGN-cortical synapses.

Algorithms↗
PubMed1987

A physiological basis for a theory of synapse modification.

The functional organization of the cerebral cortex is modified dramatically by sensory experience during early postnatal life. The basis for these modifications is a type of synaptic plasticity that may also contribute to some forms of adult learning. The question of how synapses modify according to experience has been approached by determining theoretically what is required of a modification mechanism to account for the available experimental data in the developing visual cortex. The resulting theory states precisely how certain variables might influence synaptic modifications. This insight has led to the development of a biologically plausible molecular model for synapse modification in the cerebral cortex.

Action Potentials↗
PubMed1987

A relaxation model for memory with high storage density.

We present a relaxation model for memory based on a generalized coulomb potential. The model has arbitrarily large storage capacity and, in addition, well-defined basins of attraction about stored memory states. The model is compared with the Hopfield relaxation model.

Algorithms↗
PubMed1986

Theory of an immune system retrovirus.

Human immunodeficiency virus (HIV; formerly known as human T-cell lymphotropic virus type III/lymphadenopathy-associated virus, HTLV-III/LAV), the retrovirus that infects T4-positive (helper) T cells of the immune system, has been implicated as the agent responsible for the acquired immune deficiency syndrome. In this paper, I contrast the growth of a "normal" virus with what I call an immune system retrovirus: a retrovirus that attacks the T4-positive T cells of the immune system. I show that remarkable interactions with other infections as well as strong virus concentration dependence are general properties of immune system retroviruses. Some of the consequences of these ideas are compared with observations.

Cell Division↗
PubMed1982

A neural model for category learning.

We present a general neural model for supervised learning of pattern categories which can resolve pattern classes separated by nonlinear, essentially arbitrary boundaries. The concept of a pattern class develops from storing in memory a limited number of class elements (prototypes). Associated with each prototype is a modifiable scalar weighting factor (lambda) which effectively defines the threshold for categorization of an input with the class of the given prototype. Learning involves (1) commitment of prototypes to memory and (2) adjustment of the various lambda factors to eliminate classification errors. In tests, the model ably defined classification boundaries that largely separated complicated pattern regions. We discuss the role which divisive inhibition might play in a possible implementation of the model by a network of neurons.

Animals↗
PubMed1982

Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex.

The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and postsynaptic activities but also on a slowly varying time-averaged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory environment. One does not require nonlinearity of the neuron's integrative power nor does one need to assume any particular form for intracortical circuitry. This is first illustrated in simple cases, e.g., when the environment consists of only two different stimuli presented alternately in a random manner. The following formal statement then holds: the state of the system converges with probability 1 to points of maximum selectivity in the state space. We next consider the problem of early development of orientation selectivity and binocular interaction in primary visual cortex. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further.

Animals↗
PubMed1979

A theory for the acquisition and loss of neuron specificity in visual cortex.

We assume that between lateral geniculate and visual cortical cells there exist labile synapses that modify themselves in a new fashion called threshold passive modification and in addition, non-labile synapses that contain permanent information. In the theory which results there is an increase in the specificity of response of a cortical cell when it is exposed to stimuli due to normal patterned visual experience. Non-patterned input, such as might be expected when an animal is dark-reared or raised with eyelids sutured, results in a loss of specificity, with details depending on whether noise to labile and non-labile junctions is correlated. Specificity can sometimes be regained, however, with a return of input due to patterned vision. We propose that this provides a possible explanation of experimental results obtained by Imbert and Buisseret (1975); Blakemore and Van Sluyters (1975); Buisseret and Imbert (1976); and Frégnac and Imbert (1977, 1978).

Animals↗