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

翻译：本研究构建了基于脉冲神经网络（SNN）的前额叶皮层（PFC）计算模型，以探究神经元如何适应并响应短期与长期刺激变化下的任务切换。同时，我们通过模拟脉冲架构模型中的损伤状态，研究前额叶皮层病变引发的行为缺陷。尽管已有部分前额叶皮层的计算模型，但尚未有研究利用脉冲神经网络对其进行建模。本研究采用参数接近生物合理值的脉冲神经网络，并运用无监督脉冲时序依赖可塑性（STDP）学习规则训练模型。该模型基于联结主义架构，展现出持续活动等神经现象，有助于生成短时记忆或工作记忆。我们利用这些特征，通过停用突触通路模拟病变，记录学习模式下的权重调整过程，并捕获此类条件下学习任务的准确率。所有实验均基于真实场景的Fashion MNIST（FMNIST）数据集进行训练与记录。通过本研究，我们弥合了生物真实模型与模式识别任务中高性能模型之间的差距。