Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity

Abstract

Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.

Formation and computational implications of assemblies in neural circuits

“…In this framework, plasticity at inhibitory‐to‐excitatory synapses can maintain a stable balance of excitation and inhibition after learning, such that assembly neurons have similar firing rates to other neurons in the network (Barron et al., 2017; Ramaswami, 2014). Despite this, assembly neurons show higher correlations and spiking irregularities during spontaneous activity, which can stabilise and improve the long‐term storage of the imprinted assemblies (Gallinaro & Clopath, 2021; Ocker & Doiron, 2019). The assemblies can then be accessed by disinhibitory mechanisms (Barron et al., 2016), or even be read out in seemingly quiet stages, by downstream neurons through synapses with short‐term plasticity (Gallinaro & Clopath, 2021).…”

“…Despite this, assembly neurons show higher correlations and spiking irregularities during spontaneous activity, which can stabilise and improve the long‐term storage of the imprinted assemblies (Gallinaro & Clopath, 2021; Ocker & Doiron, 2019). The assemblies can then be accessed by disinhibitory mechanisms (Barron et al., 2016), or even be read out in seemingly quiet stages, by downstream neurons through synapses with short‐term plasticity (Gallinaro & Clopath, 2021).…”

“…In summary, multiple solutions have been proposed to ensure the stability of the learned assemblies: from self‐stabilisation by activity (Litwin‐Kumar & Doiron, 2014) or spiking statistics (Gallinaro & Clopath, 2021; Ocker & Doiron, 2019), to plastic downstream readouts despite drifting representation (Rule et al., 2019) and gating of synaptic plasticity.…”

Effects of Random Inputs and Short-Term Synaptic Plasticity in a LIF Conductance Model for Working Memory Applications

“…A LIF conductance neuron with multiple inputs and coefficient of variation (CV) of the inter-spike-interval (ISI) can bring an output decoded neuron. In particular, we have found that the increase of σ Inj and σ ref can lead to an increase in the irregularity of the spike trains (see also [8]). Spike regularity can be calculated as the following coefficient of variation of the inter-spike-interval (see, e.g., [8]):…”

“…On the other hand, the dynamics of firing rate and irregularity of single neurons are closely connected [6,7]. Using a computational model to study the formation of silent assemblies in a network of spiking neurons, the authors in [8] have found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals.…”