A computational framework for epigenetic plasticity in memory.

Abstract

Memories are thought to be encoded in synaptic connections between assemblies of neurons that are reactivated during memory recall. However, in light of ongoing molecular turnover and synaptic decay, this widely accepted view cannot explain how individual ensembles are maintained over (life-)long timescales. Experimentally, learning has not only been associated with synaptic modifications among neurons, but also with epigenetic alterations of learning-related gene transcription within neurons. Although these epigenetic changes are involved in all stages of memory dynamics, they have been largely omitted in computational studies. In this update, we advocate for the integration of epigenetic mechanisms into computational models of memory. Using a recurrent neural network model that includes epigenetic plasticity as a variable, we explore the role of epigenetic priming in the maintenance of memories across long timescales; we then investigate the implications of epigenetic modifications for memory allocation and for reversing cognitive decline associated with neurodegeneration; and finally, we predict several computational advantages of including epigenetics over traditional models of synaptic memory. Overall, this paper stands as a first step towards the integration of epigenetics in computational models of memory and corroborates the experimentally derived notion that memory might not be encoded solely in synaptic weights, but rather co-encoded in epigenetic patterns within the nucleus.