Nature communications
Jan 2, 2025
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited re...
Neural networks : the official journal of the International Neural Network Society
Jan 1, 2025
Spiking Neural Networks (SNNs) are at the forefront of computational neuroscience, emulating the nuanced dynamics of biological systems. In the realm of SNN training methods, the conversion from ANNs to SNNs has generated significant interest due to ...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 31, 2024
The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell...
BMC neurology
Dec 30, 2024
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high e...
Neural networks : the official journal of the International Neural Network Society
Dec 26, 2024
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy...
International journal of neural systems
Dec 23, 2024
In the last few decades, Artificial Neural Networks have become more and more important, evolving into a powerful tool to implement learning algorithms. Spiking neural networks represent the third generation of Artificial Neural Networks; they have e...
Neural networks : the official journal of the International Neural Network Society
Dec 19, 2024
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...
Nano letters
Dec 17, 2024
The rich dynamics of magnetic materials makes them promising candidates for neural networks that, like the brain, take advantage of dynamical behaviors to efficiently compute. Here, we experimentally show that integrate-and-fire neurons can be achiev...
Neural networks : the official journal of the International Neural Network Society
Dec 16, 2024
First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrates comparable accuracy to firing-rate (FR) coding in processing complex temporal information through...
PLoS computational biology
Dec 13, 2024
Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explor...