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

通过使用脉冲神经网络（SNN）模拟前额叶皮层（PFC）的可塑性和行为缺陷，本研究桥接了生物真实模型和在模式识别任务中表现良好模型之间的差距，并探索了神经元如何适应和响应于短和长时间刺激变化下的任务