Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks

Abstract

The emergence of abstract rules from exemplars is central to the brain's capability of flexible generalization and rapid adaptation. However, the internal organizing mechanisms underlying rule abstraction remain elusive, largely due to the limitations of conventional models that lack intrinsic neuronal heterogeneity, making it hard to examine neuronal and structural differentiations. Inspired by astrocyte-mediated neuromodulation, this work introduces a hierarchically modulated recurrent spiking neural network (HM-RSNN) that can tune intrinsic neuronal properties, where a global stage simulates calcium wave-driven task-specific configuration and a local one mimics gliotransmitter-mediated fine-tuning. We conduct modeling using HM-RSNN across four cognitive tasks and rule abstraction contingent differentiation is observed at both network and neuron levels, leading to better performance compared to artificial neural networks. These findings highlight the critical role of dynamic internal organization in supporting the accomplishment of various cognitive tasks.

Tasks

Reproductions