snntorch

Sparse snnTorch implementation of Fruit Fly (Drosophila Melanogaster) Brain

Living Desktop Manager: local-first research into personal context with a spiking neural network, an interactive Observatory and a query-only MCP interface.

A differentially private spiking neural network with temporal enhanced pooling

Spiking ResNet‑18 (snnTorch) for blood‑pressure prediction from PPG. Implements PA‑B residual connections and Densely Additive Connections (Li et al., Rethinking residual connection in training large-scale spiking neural networks). Includes preprocessing for the bp‑benchmark dataset (González et al.).

Open-source Benchmark Library for Spiking Neural Network based Time-Series Forecasting.

Source code of the paper entitled "Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks through Bayesian Optimization", and presented at EPIA 2024, the 23rd International Conference on Artificial Intelligence.

Native Spiking Neural Network (SNN) implementation of the Baby Dragon Hatchling (BDH) architecture for Neuromorphic & Edge AI with Continual Learning.

Autonomous drone navigation system implemented using Spiking Neural Networks (SNN) in Verilog RTL. Includes PyTorch-to-Hardware quantization and LIF neuron models.

Source code of the paper entitled "Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection", and presented at CIARP 2024, the 27th Iberamerican Congress on Pattern Recognition.

PulsePod is an AI framework for robust ECG arrhythmia detection. It unifies MIT-BIH and PTB-XL datasets to prevent dataset shift. Featuring high-accuracy 1D-CNNs and energy-efficient Spiking Neural Networks (SNNs), it ensures reliable, real-time diagnostic performance for low-power wearable edge devices.

Official implementation of SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting, accepted at ECML PKDD 2026.

🧠⚡ NeuroEvo Cleaners Spiking Neural Networks that evolve to master complex trash collection tasks

A predictive Visual Place Recognition system using Event Forecasting Transformers and SNN encoders to enable robust robot localization during sensor dropout and total darkness.

Spiking neural network (SNN) toolkit for spike encoding, LIF model training, NIR export, inference, and deployment with PyTorch and snnTorch.

This repository trains a spiking neural network (SNN) classifier on the MNIST dataset using various spike encoding techniques. It explores different encoding schemes to convert images into spike trains and evaluates their impact on classification performance with the help of the SNNTorch module.

Learn how brain-inspired neuron models work, from the basics to hands-on code.

Adversarial robustness of Spiking Neural Networks: a comparative study of rate vs temporal coding under FGSM and PGD attacks on MNIST and CIFAR-10 (supervised by Prof. Manisha Padala).

Train Spiking Neural Networks for a Biomimetic Eye

Neuromorphic Spiking Neural Network (SNN) digit classifier built using PyTorch, snnTorch, and Streamlit. Simulates Leaky Integrate-and-Fire (LIF) neuron dynamics with interactive canvas drawing and real-time spike raster plots.