A persistent spiking neural network that lives on your desktop — 1,260 neurons, STDP learning, neuromodulators, and an LLM speech layer.

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.).

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.

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

SNN Control System for Superconducting Transmon Qubits

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.

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.

Train Spiking Neural Networks for a Biomimetic Eye

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

SOS.net (v2): YOLOv3 x SNN computer vision for optimized drone search-and-rescue.

Code used to develop and test a Spiking Neural Network model to classify MNIST and Fashion-MNIST datasets

Basic applications of SNN computer vision (snnTorch).

A Robust Framework for Arrhythmia Detection using Deep and Spiking Neural Networks

Spiking Neural Network Image Classification with SNN-Torch and Gradio. This project features a Spiking Neural Network (SNN) built using SNN-Torch for classifying images. SNNs are energy-efficient, biologically inspired models. The project aims to showcase abilities of Spiking Neural Networks to classify images and Gradio to create the model demo.

image segmentation using spiking neural network

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).

Recurrent spiking network models for predicting MNIST sequences

This repository explores Spiking Neural Networks (SNNs) and Continual Learning (CL) techniques for autonomous driving tasks, focusing on domain-incremental learning.