Assembly-based computations through contextual dendritic gating of plasticity

Abstract

Neuronal assemblies - groups of strongly connected neurons - are considered the basic building blocks of perception and memory in the brain by encoding representations of specific concepts. Despite recent evidence for the biological basis behind the existence and formation of such assemblies, computational models often fall short of showing how assemblies can be flexibly learned and combined to perform real-world computations. A prominent problem is catastrophic forgetting, where learning a new assembly can disrupt existing connectivity structure and lead to forgetting previously learned assemblies. We propose a biologically plausible computational model, where dendritic compartments (instead of neurons) are the loci for learning and inhibition gates learning in a dendrite-specific manner, to flexibly learn new stimuli without forgetting of old ones. By learning stable projections from one brain region into another and associations between different brain regions, we demonstrate how the proposed assembly framework implements the basic building blocks for diverse computations. In a visual-auditory association task, we demonstrate how the context-specific assembly computations can be used to correctly separate ambiguous stimuli based on their dendritic representations. Our models provide unique insights and predictions for how hierarchically connected brain areas use their biological components to implement flexible yet robust learning.