An efficient neural screening approach rapidly identifies circuit modules governing distinct behavioral transitions in response to pathogen exposure.

Abstract: Spiking neural networks (SNNs) are attractive algorithms that pose numerous potential advantages over traditional neural networks. One primary benefit of SNNs is that they may be run ...

Abstract: This study presents a novel approach to hyperspectral mineral classification by leveraging interpretable neural networks trained on spectral libraries for real-world mineral mapping. Using ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

As a result, the on-chip learning-based neuromorphic system achieved up to 20,000 times faster processing speed while maintaining similar interpretation accuracy to existing conventional techniques.

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

Python libraries are pre-written collections of code designed to simplify programming by providing ready-made functions for specific tasks. They eliminate the need to write repetitive code and cover ...

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...