The importance of nonlinear dendritic processing in multimodular memory networks

Abstract Recent imaging studies testify that objects are stored in the brain in a distributed network of discrete cortical areas. Motivated by these observations, we study a multi-modular associative memory network, whose functional goal is to store patterns of different coding levels, i.e., patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, the synaptic inputs to each neuron should be segregated to intramodular projections whose activations are summed up linearly, and inter-modular projections whose activations are summed up in a nonlinear fashion. This may reflect dendritic processing of distal synaptic connections. Moreover, we find that further hierarchical segregation of inter-modular connections on the dendritic tree improves memory retrieval from partial input cues, and improves resilience of the network to afferent damage.