Perceptual Learning via Modification of Cortical Top-Down Signals

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.

Sensory memory in a model of a cortical column

Julien Mayor

Sensory memory is the first step of a complex memorization process. Sensory information is buffered in a "sensory store" for a short period. Then part of it is transferred to working memory, where information can be actively processed and, eventually, through its rehearsal, can be memorized and stored in long-term memory. Sensory memory is characterized by a high capacity and a short temporal extension. In this work we show that such a transient buffering of information is well captured in models of cortical microcircuits. Sensory information stays for a short time in the perturbations it induced to the neural activity. Two fundamental characteristics of cortical columns appear to be useful to this purpose. The first one is an approximate balance of neuronal excitation and inhibition. The second one is the sparseness of cells recurrent connections. Together, these two discriminants ensure a broad set of complex dynamical behaviours of the neural network along with the presence of a phase of sustained non-oscillatory activity with very irregular spike trains in individual neurons. We show that at the vicinity of a phase transition from a quiescent state to a phase of chaotic spiking activity, global stimuli can be buffered in the "macroscopic" neuronal activity. However if the sensory input only excites a subpart of the network, efficient information buffering is made using the detailed spiking activity of every neuron in the network. The "microscopic" structure of the network is therefore needed in order to buffer sensory information. In most situations, addition of noise enhances the buffering performance and the computational power of the network. This effect is known in the physical literature as "stochastic resonance". If the network has to achieve complex transformation of its inputs, stochastic resonance is shown to be an emergent property of the population of neurons. Both the very irregular spiking activity present in vivo and the balanced excitation and inhibition measured in cortical columns have a new functional relevance in being the neuronal substrate of an efficient sensory memory .

EPFL2005

Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells

Henry Markram, Gilad Silberberg

Reliable activation of inhibitory pathways is essential for maintaining the balance between excitation and inhibition during cortical activity. Little is known, however, about the activation of these pathways at the level of the local neocortical microcircuit. We report a disynaptic inhibitory pathway among neocortical pyramidal cells (PCs). Inhibitory responses were evoked in layer 5 PCs following stimulation of individual neighboring PCs with trains of action potentials. The probability for inhibition between PCs was more than twice that of direct excitation, and inhibitory responses increased as a function of rate and duration of presynaptic discharge. Simultaneous somatic and dendritic recordings indicated that inhibition originated from PC apical and tuft dendrites. Multineuron whole-cell recordings from PCs and interneurons combined with morphological reconstructions revealed the mediating interneurons as Martinotti cells. Martinotti cells received facilitating synapses from PCs and formed reliable inhibitory synapses onto dendrites of neighboring PCs. We describe this feedback pathway and propose it as a central mechanism for regulation of cortical activity.

2007

Perceptual learning: why mice do not play backgammon

Elisa Tartaglia

Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscience. Thanks to its simple nature, perceptual learning is often considered a basic form of learning, and an optimal starting point to understand more complex forms of plasticity. From a computational perspective, perceptual learning is usually described with simple neural network architectures. Here I demonstrate by empirical results and theoretical considerations why current computational models are inadequate to describe perceptual learning. Paradoxically, simple computational models often largely outperform human performance. While very simple feed-forward neural networks can learn any stimulus-response mapping, the human brain cannot. Common neural networks consider the learning process a mere computational problem, primarily focusing on stimulus response mappings. However, I will show that perceptual learning is a more complex phenomenon. For example, while neural networks can learn only one task per network, in real learning situations, human observers often have to learn multiple tasks by using the very same neurons. Hence, more plausible computational approaches that account for the complex learning situations of human observers, need to be developed.

EPFL2009

Sensory memory in a model of a cortical column

Julien Mayor

EPFL2005