A 3-layer SNN code for performing MNIST handwritten digit recognition using a supervised spike based learning rule

[Pattern Recognition'23] AC2AS: Activation Consistency Coupled ANN-SNN Framework for Fast and Memory-Efficient SNN Training

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Deep learning production system that predicts with 88% confidence whether a client will choose a term deposit. The system uses spiking neural networks (SNNs), a bio-inspired approach to neural computation that offers unique advantages for temporal pattern recognition.

This project implements a Spiking Neural Network (SNN) model in C++, integrated with Python using pybind11. It includes dynamic pattern recognition and real-time data processing capabilities(kinda); The repository provides comprehensive examples, unit tests, and a detailed Python interface for flexible usage and development.