Models of Metaplasticity: A Review of Concepts

By Pierre Yger, Matthieu Gilson. 2015

80 citations recorded by the source corpus.

Part of hippocampal and cortical plasticity is characterized by synaptic modifications that depend on the joint activity of the pre- and post-synaptic neurons. To which extent those changes are determined by the exact timing and the average firing rates is still a matter of debate; this may vary from brain area to brain area, as well as across neuron types. However, it has been robustly observed both in vitro and in vivo that plasticity itself slowly adapts as a function of the dynamical context, a phenomena commonly referred to as metaplasticity. An alternative concept considers the regulation of groups of synapses with an objective at the neuronal level, for example, maintaining a given average firing rate. In that case, the change in the strength of a particular synapse of the group (e.g., due to Hebbian learning) affects others' strengths, which has been coined as heterosynaptic plasticity. Classically, Hebbian synaptic plasticity is paired in neuron network models with such mechanisms in order to stabilize the activity and/or the weight structure. Here, we present an oriented review that brings together various concepts from heterosynaptic plasticity to metaplasticity, and show how they interact with Hebbian-type learning. We focus on approaches that are nowadays used to incorporate those mechanisms to state-of-the-art models of spiking plasticity inspired by experimental observations in the hippocampus and cortex. Making the point that metaplasticity is an ubiquitous mechanism acting on top of classical Hebbian learning and promoting the stability of neural function over multiple timescales, we stress the need for incorporating it as a key element in the framework of plasticity models. Bridging theoretical and experimental results suggests a more functional role for metaplasticity mechanisms than simply stabilizing neural activity.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Moreover, the rate at which the synaptic scaling process itself causes synaptic strengths to scale must be finely tuned to a narrow parameter regime that is neither too fast nor too slow [31 ,32 ,33,53]; if too slow, then stabilization is not possible, while if too fast, the stabilization process ov”
Emergent Dynamical Properties of the BCM Learning Rule (2017, methodology): “Similar mean field models have been made, but in terms of synaptic weights; see, for example Yger and Gilson [32].”
Electronic supplementary material: Hebbian plasticity requires compensatory processes on multiple timescales (2016, methodology): “This analytical formalism is quite general [9] (see Yger and Gilson [10] for a review) and can be applied to spike-timing-dependent plasticity (STDP), spiking neurons and spiking neural networks.”