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Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep activity at healthy, low levels, plasticity must therefore incorporate homeostatic control mechanisms. We find in numerical simulations of recurrent networks with a realistic triplet-based spike-timing-dependent plasticity rule (triplet STDP) that homeostasis has to detect rate changes on a timescale of seconds to minutes to keep the activity stable. We confirm this result in a generic mean-field formulation of network activity and homeostatic plasticity. Our results strongly suggest the existence of a homeostatic regulatory mechanism that reacts to firing rate changes on the order of seconds to minutes.

Original publication

DOI

10.1371/journal.pcbi.1003330

Type

Journal article

Journal

PLoS Comput Biol

Publication Date

2013

Volume

9

Keywords

Action Potentials, Computational Biology, Computer Simulation, Homeostasis, Models, Neurological, Neuronal Plasticity, Neurons, Synapses