Fast Coding of Orientation in Primary Visual Cortex

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple ‘race to threshold’ readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made

Charge relaxation in polyampholytes of various statistics

We discuss theoretically the relaxation of charge fluctuations in polyampholyte solutions. It has been shown previously by some of us (J. Wittmer et al. , Europhys. Lett. 24, 263 (1993)) that the charge distribution along the polyampholyte backbone has a dramatic influence on the polarization energy and hence on the solubility. Here it is demonstrated that a similar effect exists for the charge relaxation. The charge relaxation mechanism qualitatively depends on the statistics: for alternating polyampholytes the relaxation is mainly due to local dipole inversion and is not primarily driven by electrostatic interactions, whereas for random polyampholytes it is driven by electrostatic interactions. Intermediate statistics (with short-ranged (exponential) correlations) appear as a combination of these two limiting cases: short-wavelength modes are insensitive to the loss of correlations along the backbone, whereas long-wavelength modes correspond to a random statistics with renormalized charges. The relaxation of the dielectric constant is also calculated

Dynamic Formation of Self-Organizing Maps

International audienceIn this paper, an original dynamical system derived from dynamic neural fields is studied in the context of the formation of topographic maps. This dynamical system overcomes limitations of the original Self-Organizing Map (SOM) model of Kohonen. Both competition and learning are driven by dynamical systems and performed continuously in time. The equations governing competition are shown to be able to reconsider dynamically their decision through a mechanism rendering the current decision unstable, which allows to avoid the use of a global reset signal

Top-Down Inputs Enhance Orientation Selectivity in Neurons of the Primary Visual Cortex during Perceptual Learning

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections

A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization

Experience with transient stimuli leads to stronger neural responses that also rise and fall more sharply in time. This sharpening enhances the processing of transients and may be especially relevant for speech perception. We consider a learning rule for inhibitory connections that promotes this sharpening effect by adjusting these connections to maintain a target homeostatic level of activity in excitatory neurons. We analyze this rule in a recurrent network model of excitatory and inhibitory units. Strengthening inhibitory→excitatory connections along with excitatory→excitatory connections is required to obtain a sharpening effect. Using the homeostatic rule, we show that repeated presentations of a transient signal will “teach” the network to respond to the signal with both higher amplitude and shorter duration. The model also captures reorganization of receptive fields in the sensory hand area after amputation or peripheral nerve resection

Perceptual priming leads to reduction of gamma frequency oscillations

Oscillations of neural activity are ubiquitous in the brain and are critical for normal cognitive function. In the visual system, repetitive presentation of a stimulus results in the reduction of power elicited in the gamma frequency band. However, this reduction does not result in degradation of perception; on the contrary, perception is improved by prior experience with the stimulus. To explain how reduction of gamma frequency oscillations, observed in priming experiments, can lead to improvement in behavior, we assume that visual processing takes place in two distinct stages: representation sharpening in the early visual areas and competitive interaction among representations in the higher visual areas and the prefrontal cortex. Here, we present a network model of spiking neurons that demonstrates how stimulus repetition leads to a decrease in power of the local field potential oscillations in the gamma frequency range in the early layer and also improves network response by reducing the latency to reach a decision in the higher area