Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

Two RIKEN researchers have used a scheme for simplifying data to mimic how the brain of a fruit fly reduces the complexity of ...

The post New Japanese Study Suggests That Fungi Are Smarter Than You Might Expect, With A Neural-Style Network Connecting ...

This paper explores the innovative application of Quantum Convolutional Neural Networks (QCNN) in the classification of the MNIST fashion dataset. Quantum computing presents novel opportunities for ...

This research presents a siamese neural network approach for predicting 199 neurotoxicity related targets of environmental chemicals, based on the primary structure of proteins and the molecular st ...

NumberRecognition is a project aimed at recognizing handwritten digits from the MNIST dataset using PyTorch. It includes scripts for training and inference, along with utilities for dataset ...

In neural circuits, recurrent connectivity plays a crucial role in network function and stability.However, existing recurrent spiking neural networks (RSNNs) ...

The MNIST dataset is a crucial tool for machine learning experiments. It has 60,000 grayscale images for training and 10,000 for testing, often used for image classification using neural networks and ...