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Available from: Hecke Schrobsdorff, Oct 09, 2015
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    • "We will discuss here an algorithm to achieve compromise between a critical dynamics that can be seen as exploring the spaces of neural activation patterns, and the attractor dynamics that we assume to underlay the retrieval of content from memory. The present paper continues upon earlier work (Schrobsdorff et al., 2009; Dasgupta and Herrmann, 2011), where the preliminary simulation results were discussed. In our study for the first time conclusive numerical representations are presented, several learning mechanisms are compared and the capacity limit is considered. "
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    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.
    Frontiers in Computational Neuroscience 07/2013; 7:87. DOI:10.3389/fncom.2013.00087 · 2.20 Impact Factor