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Biomedical subjects

L N Cooper

Publications and source records attributed to L N Cooper.

At least 19 recordsLinked to original sources
PubMed2005

Targeting c-Myc-activated genes with a correlation method: detection of global changes in large gene expression network dynamics.

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.

Analysis of Variance↗
PubMed2001

A biophysical model of bidirectional synaptic plasticity: dependence on AMPA and NMDA receptors.

In many regions of the brain, including the mammalian cortex, the magnitude and direction of activity-dependent changes in synaptic strength depend on the frequency of presynaptic stimulation (synaptic plasticity), as well as the history of activity at those synapses (metaplasticity). We present a model of a molecular mechanism of bidirectional synaptic plasticity based on the observation that long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD) correlate with the phosphorylation/dephosphorylation of sites on the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor subunit protein GluR1. The primary assumption of the model, for which there is wide experimental support, is that postsynaptic calcium concentration and consequent activation of calcium-dependent protein kinases and phosphatases are the triggers for the induction of LTP/LTD. As calcium influx through the n-methyl-d-aspartate (NMDA) receptor plays a fundamental role in the induction of LTP/LTD, changes in the properties of NMDA receptor-mediated calcium influx will dramatically affect activity-dependent synaptic plasticity (metaplasticity). We demonstrate that experimentally observed metaplasticity can be accounted for by activity-dependent regulation of NMDA receptor subunit composition and function. Our model produces a frequency-dependent LTP/LTD curve with a sliding synaptic modification threshold similar to what has been proposed theoretically by Bienenstock, Cooper, and Munro and observed experimentally.

Animals↗
PubMed2000

Statistics of lateral geniculate nucleus (LGN) activity determine the segregation of ON/OFF subfields for simple cells in visual cortex.

The receptive fields for simple cells in visual cortex show a strong preference for edges of a particular orientation and display adjacent excitatory and inhibitory subfields. These subfields are projections from ON-center and OFF-center lateral geniculate nucleus cells, respectively. Here we present a single-cell model using ON and OFF channels, a natural scene environment, and synaptic modification according to the Bienenstock, Cooper, and Munro (BCM) theory. Our results indicate that lateral geniculate nucleus cells must act predominantly in the linear region around the level of spontaneous activity, to lead to the observed segregation of ON/OFF subfields.

Animals↗
PubMed2000

Structured long-range connections can provide a scaffold for orientation maps.

In the visual cortex of the cat and ferret, it is established that maturation of orientation selectivity is shaped by experience-dependent plasticity. However, recent experiments indicate that orientation maps are remarkably stable and experience-independent. We present a model to account for these seemingly paradoxical results. In this model, a scaffold consisting of non-isotropic lateral connections is laid down in horizontal circuitry before visual experience. These lateral connections provide an experience-independent framework for the developing orientation maps by inducing a broad orientation tuning bias in the model neurons. Experience-dependent plasticity of the thalamocortical connections sharpens the tuning while the preferred orientation of the neurons remains unchanged. This model is verified by computer simulations in which the scaffolds are generated both artificially and inferred from experimental optical imaging data. The plasticity is modeled by the BCM synaptic plasticity rule, and the input environment consists of natural images. We use this model to provide a possible explanation of the recent observation in which two eyes without common visual experience develop similar orientation maps. Finally, we propose an experiment involving the disruption of lateral connections to distinguish this model from models proposed by others.

Animals↗
PubMed2000

Formation of direction selectivity in natural scene environments.

Most simple and complex cells in the cat striate cortex are both orientation and direction selective. In this article we use single-cell learning rules to develop both orientation and direction selectivity in a natural scene environment. We show that a simple principal component analysis rule is inadequate for developing direction selectivity, but that the BCM rule as well as similar higher-order rules can. We also demonstrate that the convergence of lagged and nonlagged cells depends on the velocity of motion in the environment, and that strobe rearing disrupts this convergence, resulting in a loss of direction selectivity.

Algorithms↗
PubMed1999

The role of presynaptic activity in monocular deprivation: comparison of homosynaptic and heterosynaptic mechanisms.

Although investigations in computational neuroscience have been extensive, the opportunity (that has made such a marked difference in physical sciences) to test detailed and subtle quantitative consequences of a theory against experimental results is rare. In this paper, we outline a testable consequence of two contrasting theories of synaptic plasticity applied to the disconnection in visual cortex of the closed eye in monocular deprivation. This disconnection is sometimes thought to be the consequence of a process that stems from a competition of inputs for a limited resource such as neurotrophin. Such a process leads to what we call spatial competition, or heterosynaptic synaptic modification. A contrasting view-exemplified by the Bienenstock, Cooper, and Munro (BCM) theory-is that patterns of input activity compete in the temporal domain. This temporal competition is homosynaptic and does not require a conserved resource. The two mechanisms, homosynaptic and heterosynaptic, are the distinguishing characteristics of two general classes of learning rules we explore by using a realistic environment composed of natural scenes. These alternative views lead to opposite dependence on the level of presynaptic activity of the rate of disconnection of the closed eye in monocular deprivation. This strong and testable consequence sets the stage for a critical distinguishing experiment. This experiment has been done and supports the second view. These results have important implications for the processes of learning and memory storage in neocortex.

Animals↗
PubMed1999

Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons.

We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.

Models, Neurological↗
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↗