title: Critical dynamics in associative memory networks.
abstract: Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network only endowed with Hebbian learning does not allow for simultaneous information storage and criticality. However, the critical regime can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.
authors.affiliation: Bernstein Center for Computational Neuroscience Göttingen, Germany ; Max Planck Institute for Dynamics and Self-Organization Göttingen, Germany.
authors.forename: Maximilian
authors.initials: M
authors.lastname: Uhlig
authors.forename: Anna
authors.initials: A
authors.lastname: Levina
authors.forename: Theo
authors.initials: T
authors.lastname: Geisel
authors.forename: J Michael
authors.initials: JM
authors.lastname: Herrmann
keywords: Hebbian learning; SOC; associative memory; dynamical synapses; homeostatic learning
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