Speaker
Description
Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. We propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. We develop various statistical physics descriptions to quantitatively model the drift of assemblies in single brain regions and throughout the brain. This allows to predict the future dynamics of the neurobiologically observed initial drift of memory representations, which is usually interpreted as a sign of memory consolidation processes.
