Inhgrams for engrams: co-active inhibitory-inhibitory plasticity shapes inhibitory assemblies that stabilize and recall embedded engrams through disinhibition

Inhibitory neurons have been widely understood to play a supporting role in neural function and memory formation, but recent advances highlight that memories are encoded by both excitatory and inhibitory neurons (EI assemblies), carving out a bigger role for inhibition beyond mere stabilization. However, the computations enabled by such EI assemblies are still unclear, and the synaptic plasticity rules that could sustain and retrieve memories are unknown. Here, we construct a computational model for EI assembly recall and investigate the computational benefits of such mixed engrams over classical, excitatory-only engrams. Towards this end, we consider large recurrent spiking networks with symmetrical Hebbian synaptic plasticity at both inhibitory-to-inhibitory (I-to-I) and inhibitory-to-excitatory (I-to-E) synapses. The conjunction of these rules can robustly stabilize embedded EI assemblies in the asynchronous irregular regime. Assemblies can then be reactivated by two distinct mechanisms: direct stimulation of the engram or disinhibition through the inhgram. Both mechanisms of recall lead to reliable pattern completion and separation. Crucially, we show that networks that lack I-to-I plasticity show weak recall and cannot discriminate between overlapping engrams via disinhibitory activation. This suggests that inhibitory plasticity can facilitate recall of overlapping engrams, with inhibitory neurons controlling multiple excitatory engrams. Furthermore, we show that inhgrams can emerge from pre-existing E-I-E loops in the network's random connectivity. Our work proposes that inhibitory plasticity is a plausible mechanism for high-quality disinhibitory recall, allowing inhibitory neurons to selectively control multiple excitatory engrams through synapse-specific plasticity rules.