2005/08/13 by J. M. Cortes, Jesús M. Cortés, Joaquı́n J. Torres +12
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Physics and Astronomy · #FOS: Biological sciences #Neural Networks and Applications #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.q-bio/0508013
12 pages, 6 figures. To appear in Neural Computation, 2005
arxiv created 2005/08/13 · openalex publication_date 2005/08/13 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short-time scale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short-time scale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason for this is that, in general, the presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily scape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.