Year: 2007
Type: article
Abstract: We investigate dynamical systems characterized by a time series of distinct semi-stable activity patterns, as they are observed in cortical neural activity patterns. We propose and discuss a general mechanism allowing for an adiabatic continuation between attractor networks and a specific adjoined transient-state network, which is strictly dissipative. Dynamical systems with transient states retain functionality when their working point is autoregulated—avoiding prolonged periods of stasis or drifting into a regime of rapid fluctuations. We show, within a continuous-time neural network model, that a single local updating rule for online learning allows simultaneously (i) for information storage via unsupervised Hebbian-type learning, (ii) for adaptive regulation of the working point and (iii) for the suppression of runaway synaptic growth. Simulation results are presented; the spontaneous breaking of time-reversal symmetry and link symmetry are discussed.
Source: New Journal of Physics
Author: Claudius Gros
Institution: Goethe University Frankfurt
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,610
OpenAlex ID: W2014093524