Shypd.aiThe largest open index of AI toolsSpiking-Neural-Network is an AI Frameworks & Infra tool that provides a pure Python implementation of hardware-efficient spiking neural networks. It includes modified learning and prediction rules suitable for on-chip learning and prediction.
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Spiking-Neural-Network offers a pure Python implementation of hardware-efficient spiking neural networks (SNNs). This tool focuses on developing a network capable of on-chip learning and prediction, utilizing modified learning and prediction rules that are energy-efficient and realizable on hardware. It incorporates the Spike-Time Dependent Plasticity (STDP) algorithm for network training, a biological process that modifies neural connections based on spike timing. The simulator supports classification tasks, employing a 'winner-takes-all' strategy for distinguishable results. Key features include neuron, synapse, receptive field, and spike train elements, along with functionalities for multi-class classification, variable threshold normalization, and lateral inhibition. The project also explores the generative property of SNNs to visualize learned patterns and discusses critical parameters like learning rate and weight initialization.
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