Abstract
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.
Highlights
The brain is made of billions of neurons able to efficiently process the huge flow of information impinging continuously on sensory modalities, extracting relevant data, and producing appropriately timed responses
In controlled in vitro experiments, it has been shown that LTP and long-term depression (LTD) depend on the precise timing of pre- and postsynaptic spikes (Markram et al, 1997; Bi and Poo, 1998), leading to the concept of timing-LTP/LTD or spike-timing-dependent plasticity (STDP)
While we analyzed the dynamical behavior of the learning rules for the mean weight to assess their implications for stability, we examine the situation for two inputs in order to study how competition can be affected by this interaction between homeostatic and Hebbian learning
Summary
The brain is made of billions of neurons able to efficiently process the huge flow of information impinging continuously on sensory modalities, extracting relevant data, and producing appropriately timed responses. Even during development (Corlew et al, 2007; Wang et al, 2012) or when lesioned (Young et al, 2007; Beck and Yaari, 2008), the brain has the striking capability to adapt in order to maintain the stability of neural functions This slow adaptation, acting at a timescale of hours or days (Turrigiano and Nelson, 2000; Davis, 2006) is performed in conjunction with fast changes often observed in the so called Hebbian learning (Hebb, 1949). Maintaining the stability only being one of the requirements for proper behavior, we will discuss how homeostatic constraints can be used to adjust the function implemented by the neural circuits
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