Models of Metaplasticity: A Review of Concepts
Résumé
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.
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