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

• URL: http://arxiv.org/abs/2503.03784v1
• Date: Wed, 05 Mar 2025 00:44:34 GMT
• Title: Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control
• Authors: Ashwin Viswanathan Kannan, Madhumitha Ganesan,
• Abstract summary: This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics.<n>The model incorporates biologically realistic features, including lateral inhibition, adaptive synaptic weights, and precise parameterization within physiologically relevant ranges.<n>By following this tutorial, researchers can develop and extend biologically inspired SNN models for studying cognitive processes and neural adaptation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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