  In this study, we build a computational model of Prefrontal Cortex (PFC)
using Spiking Neural Networks (SNN) to understand how neurons adapt and respond
to tasks switched under short and longer duration of stimulus changes. We also
explore behavioral deficits arising out of the PFC lesions by simulating
lesioned states in our Spiking architecture model. Although there are some
computational models of the PFC, SNN's have not been used to model them. In
this study, we use SNN's having parameters close to biologically plausible
values and train the model using unsupervised Spike Timing Dependent Plasticity
(STDP) learning rule. Our model is based on connectionist architectures and
exhibits neural phenomena like sustained activity which helps in generating
short-term or working memory. We use these features to simulate lesions by
deactivating synaptic pathways and record the weight adjustments of learned
patterns and capture the accuracy of learning tasks in such conditions. All our
experiments are trained and recorded using a real-world Fashion MNIST (FMNIST)
dataset and through this work, we bridge the gap between bio-realistic models
and those that perform well in pattern recognition tasks
