Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control

TL;DR

The paper addresses cognitive flexibility and task-switching by presenting a biologically plausible spiking neural network (SNN) model that uses Leaky Integrate-and-Fire (LIF) neurons, lateral inhibition, and unsupervised Spike Timing-Dependent Plasticity (STDP) to simulate executive control. It introduces a two-layer SNN framework with an excitatory processing layer and an inhibitory regulatory layer, implemented in Brian2, and evaluates performance on real-world task data under dynamic switching paradigms. Key contributions include demonstrating how STDP-driven synaptic adaptation interacts with inhibitory control to enable stable task reconfiguration, quantifying adaptation via metrics like Task Separation Index and retention measures, and showing how switching intervals affect learning retention and neural dynamics. The findings provide a computational and methodological foundation for extending biologically inspired SNNs to study cognitive processes and neural adaptation with potential applications in cognitive computing and neuroscience research.

Abstract

Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics within the cognitive control network. The model incorporates biologically realistic features, including lateral inhibition, adaptive synaptic weights through unsupervised Spike Timing-Dependent Plasticity (STDP), and precise neuronal parameterization within physiologically relevant ranges. The SNN is implemented using Leaky Integrate-and-Fire (LIF) neurons, which represent excitatory (glutamatergic) and inhibitory (GABAergic) populations. We utilize two real-world datasets as tasks, demonstrating how the network learns and dynamically switches between them. Experimental design follows cognitive psychology paradigms to analyze neural adaptation, synaptic weight modifications, and emergent behaviors such as Long-Term Potentiation (LTP), Long-Term Depression (LTD), and Task-Set Reconfiguration (TSR). Through a series of structured experiments, this tutorial illustrates how variations in task-switching intervals affect performance and multitasking efficiency. The results align with empirically observed neuronal responses, offering insights into the computational underpinnings of executive function. By following this tutorial, researchers can develop and extend biologically inspired SNN models for studying cognitive processes and neural adaptation.