title: Noise-enhanced categorization in a recurrently reconnected neural network.
abstract: We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.
authors.affiliation: Max-Planck Institut für Physik Komplexer Systeme, Nöthnitzerstrasse 38, 01187 Dresden, Germany. chris@mpipks-dresden.mpg.de
authors.forename: Christopher
authors.initials: C
authors.lastname: Monterola
authors.forename: Martin
authors.initials: M
authors.lastname: Zapotocky