A computational framework for epigenetic plasticity in memory.

Memories are thought to be encoded in synaptic connections between assemblies of neurons that are reactivated during memory recall. However, 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 epigenetics in 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 implication 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 memories. 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 may not be solely encoded in synaptic weights, but rather co-encoded in epigenetic patterns within the nucleus.