Context-Dependent Computations in Spiking Neural Networks with Apical Modulation

Romain Ferrand, Maximilian Baronig, Thomas Limbacher, Robert Legenstein

Abstract: Neocortical pyramidal neurons integrate two distinct streams of information. Bottom-up information arrives at their basal dendrites, and resulting neuronal activity is modulated by top-down input that targets the apical tufts of these neurons and provides context information. Although this integration is essential for cortical computations, its relevance for the computations in spiking neural networks has so far not been investigated. In this article, we propose a simple spiking neuron model for pyramidal cells. The model consists of a basal and an apical compartment, where the latter modulates activity of the former in a multiplicative manner. We show that this model captures the experimentally observed properties of top-down modulated activity of cortical pyramidal neurons. We evaluated recurrently connected networks of such neurons in a series of context-dependent computation tasks. Our results show that the resulting novel spiking neural network model can significantly enhance spike-based context-dependent computations.