用脉冲神经网络模拟大脑任务切换，揭示认知控制的计算机制。
Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control
- 构建含突触可塑性的脉冲神经网络，模拟大脑任务切换行为。
- 实验显示任务切换间隔影响表现，与真实神经反应一致。
- 适合神经科学与类脑计算研究者学习建模认知灵活性。
理解神经系统的认知灵活性与任务切换机制，需依赖生物合理计算模型。本教程逐步介绍如何构建一个脉冲神经网络（SNN），模拟认知控制网络中的任务切换动态。模型包含侧向抑制、通过无监督脉冲时序依赖可塑性（STDP）实现的自适应突触权重，以及生理范围内的精确神经元参数化。采用漏积分-放电（LIF）神经元表示兴奋性（谷氨酸能）和抑制性（GABA能）神经元群体。利用两个真实数据集作为任务，展示网络如何学习并动态切换。实验设计遵循认知心理学范式，分析神经适应、突触权重变化及涌现行为，如长时程增强（LTP）、长时程抑制（LTD）和任务设置重构（TSR）。系列实验表明，任务切换间隔的变化影响性能与多任务效率。结果与实证观察到的神经响应相符，揭示了执行功能的计算基础。本教程帮助研究人员开发和拓展生物启发的SNN模型以研究认知过程与神经适应。
原文摘要 · Abstract (English)
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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