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

The ability to adapt and switch between tasks is a fundamental Empirical studies further established the prefrontal cortex aspect of cognitive flexibility, shaping decision-making (PFC) as a key region in task-switching, with experiments such and behavioral efficiency in dynamic environments. Taskswitching as the Wisconsin Card Sorting Test (WCST) demonstrating its has been widely studied across disciplines such as role in adaptive behavior [14]-[16]. Spiking Neural Networks psychology, cognitive neuroscience, and artificial intelligence (SNNs) have emerged as a biologically realistic approach to [1], [2]. While humans often shift between tasks seamlessly, modeling neural dynamics, particularly due to their ability to performance variations arise depending on prior experience, replicate synaptic plasticity mechanisms such as Spike Timing-task familiarity, and cognitive load. Understanding these processes Dependent Plasticity (STDP) [10], [17]. Prior studies have requires computational models that can capture the successfully applied SNNs to pattern recognition and classification underlying neural mechanisms driving adaptive control and tasks [18] and have modeled sensory processing systems decision-making. Empirical studies have identified increased like the mammalian olfactory system [19]. These findings neural activity in the cognitive control network, particularly in establish a computational foundation for implementing taskswitching the prefrontal cortex (PFC), when engaging in task-switching models with biologically grounded learning dynamics.