Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Friedemann Zenke,Wulfram Gerstner,Guillaume Hennequin,Abigail Morrison

doi:10.1371/journal.pcbi.1003330

Friedemann Zenke, Wulfram Gerstner + Show 2 more

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https://doi.org/10.1371/journal.pcbi.1003330

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Abstract

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.

Highlights

The awake cortex is constantly active, even in the absence of external inputs
In the beginning we focus on a metaplastic mechanism that regulates the amount of synaptic long term depression (LTD) homeostatically
By systematically varying the time constant of the homeostatic rate detector, we find that stability of the background state requires homeostasis to act on a timescale of minutes

Summary

Introduction

The awake cortex is constantly active, even in the absence of external inputs. This baseline activity, commonly referred to as the ‘‘background state’’, is characterized by low synchrony at the population level and highly irregular firing of single neurons. The background state is currently understood as the asynchronous and irregular (AI) firing regime resulting from a dynamic balance of excitation and inhibition in recurrent neural networks [6,7,8,9]. Even small changes in the amount of excitation can disrupt the background state [7,10]. Changes in excitation can arise from Hebbian plasticity of excitatory synapses: Subsets of jointly active neurons form strong connections with each other which is thought to be the neural substrate of memory [11]. The emergent positive feedback loop renders this form of plasticity unstable and makes it hard to reconcile with the stability of the background state [12]

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