Intrinsic neural excitability biases allocation and overlap of memory engrams.

Memories are thought to be stored in neural ensembles known as engrams that are specifically reactivated during memory recall. Recent studies have found that memory engrams of two events that happened close in time tend to overlap in the hippocampus and the amygdala, and these overlaps have been shown to support memory linking. It has been hypothesised that engram overlaps arise from the mechanisms that regulate memory allocation itself, involving neural excitability, but the exact process remains unclear. Indeed, most theoretical studies focus on synaptic plasticity and little is known about the role of intrin-sic plasticity, which could be mediated by neural excitability and serve as a complementary mechanism for forming memory engrams. Here, we developed a rate-based recurrent neural network that includes both synaptic plasticity and neural excitability. We obtained structural and functional overlap of memory engrams for contexts that are presented close in time, consistent with experimental and computational studies. We then investigated the role of excitability in memory allocation at the network level and un-veiled competitive mechanisms driven by inhibition. This work suggests mechanisms underlying the role of intrinsic excitability in memory allocation and linking, and yields predictions regarding the formation and the overlap of memory engrams. Significance statement In the brain, memories are not formed in isolation from each other. For example, two memories of events that happened close in time tend to be linked, so that recalling one memory leads to recall of the second one. Although memories are thought to be formed by reinforcement of synapses among neurons, understanding memory linking requires us to consider intrinsic properties of the neurons themselves. In this study, we modelled a neural network aiming at explaining how memories are formed and linked in the brain. This model is able to reproduce experimental results and allows us to make predictions about how to link or dissociate memories.