Pre-integration lateral inhibition enhances unsupervised learning

A large and influential class of neural network architectures use post-integration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through pre-integration lateral inhibition, does provide appropriate coding properties and can be used to efficiently learn such representations. Furthermore, this architecture is consistent with both neuro-anatomical and neuro-physiological data. We thus argue that pre-integration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible

Linking Visual Development and Learning to Information Processing: Preattentive and Attentive Brain Dynamics

National Science Foundation (SBE-0354378); Office of Naval Research (N00014-95-1-0657

Dendritic inhibition enhances neural coding properties.

The presence of a large number of inhibitory contacts at the soma and axon initial segment of cortical pyramidal cells has inspired a large and influential class of neural network model which use post-integration lateral inhibition as a mechanism for competition between nodes. However, inhibitory synapses also target the dendrites of pyramidal cells. The role of this dendritic inhibition in competition between neurons has not previously been addressed. We demonstrate, using a simple computational model, that such pre-integration lateral inhibition provides networks of neurons with useful representational and computational properties which are not provided by post-integration inhibition

Tuning the average path length of complex networks and its influence to the emergent dynamics of the majority-rule model

We show how appropriate rewiring with the aid of Metropolis Monte Carlo computational experiments can be exploited to create network topologies possessing prescribed values of the average path length (APL) while keeping the same connectivity degree and clustering coefficient distributions. Using the proposed rewiring rules we illustrate how the emergent dynamics of the celebrated majority-rule model are shaped by the distinct impact of the APL attesting the need for developing efficient algorithms for tuning such network characteristics.Comment: 10 figure

A model of emotional influence on memory processing.

To survive in a complex environment, agents must be able to encode information about the utility value of the objects they meet. We propose a neuroscience-based model aiming to explain how a new memory is associated to an emotional response. The same theoretical framework also explains the effects of emotion on memory recall. The originality of our approach is to postulate the presence of two central processing units (CPUs): one computing only emotional information, and the other mainly concerned with cognitive processing. The emotional CPU, which is phylogenetically older, is assumed to modulate the cognitive CPU, which is more recent. The article first deals with the cognitive part of the model by highlighting the set of processes underlying memory recognition and storage. Then, building on this theoretical background, the emotional part highlights how the emotional response is computed and stored. The last section describes the interplay between the cognitive and emotional systems