Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A Hebbian-based reinforcement learning framework for spike-timing-dependent synapses
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Temporal finite-state machines: a novel framework for the general class of dynamic networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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A simple hypothetical energy function is proposed for a dynamic synaptic model. It is an approach based on the theoretical thermodynamic principles that are conceptually similar to the Hopfield ones. We show that using this approach a synapse exposes stable operating points in terms of its excitatory postsynaptic potential (EPSP) as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). The results illustrate that a synaptic dynamical model has more than one stable operating point regarding the postsynaptic energy transfer. This proposes a novel explanation of the observed synchronous activities within networks regarding the synaptic (coupling) functionality.