AIMC Topic: Action Potentials

Clear Filters Showing 41 to 50 of 521 articles

CS-QCFS: Bridging the performance gap in ultra-low latency spiking neural networks.

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 ...
Humans Models, Neurological Action Potentials Neurons Neural Networks, Computer Algorithms
View on PubMed DOI

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

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...
Alzheimer Disease Ion Channels Patch-Clamp Techniques Kinetics Neurons Deep Learning Action Potentials Humans
View on PubMed DOI

A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.

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...
Parkinson Disease Neurons Algorithms Models, Neurological Action Potentials Humans Neural Networks, Computer
View on PubMed DOI

Free-space optical spiking neural network.

PloS one Dec 30, 2024
Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an...
Models, Neurological Action Potentials Algorithms Brain Humans Neural Networks, Computer
View on PubMed DOI

Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios.

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, Computer Algorithms Action Potentials Humans Epilepsy Neurons
View on PubMed DOI

Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal of cardiovascular electrophysiology Dec 23, 2024
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...
Atrial Fibrillation Heart Rate Catheter Ablation Action Potentials Predictive Value of Tests Recurrence Middle Aged Risk Assessment Treatment Outcome Cluster Analysis Time Factors Risk Factors Aged Male Female Reproducibility of Results Humans Decision Support Techniques Phenotype Machine Learning
View on PubMed DOI

Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

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...
Deep Learning Neuronal Plasticity Action Potentials Neurons Models, Neurological Humans Neural Networks, Computer Computer Simulation Reinforcement, Psychology
View on PubMed DOI

Spintronic Artificial Neurons Showing Integrate-and-Fire Behavior with Reliable Cycling Operation.

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, Computer Action Potentials Neurons
View on PubMed DOI

Enhanced accuracy in first-spike coding using current-based adaptive LIF neuron.

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...
Neurons Neural Networks, Computer Models, Neurological Action Potentials Humans
View on PubMed DOI

Damage explains function in spiking neural networks representing central pattern generator.

Journal of neural engineering Dec 16, 2024
Complex biological systems have evolved to control movement dynamics despite noisy and unpredictable inputs and processing delays that necessitate forward predictions. The staple example in vertebrates is the locomotor control emerging from interacti...
Nerve Net Central Pattern Generators Cats Locomotion Neural Networks, Computer Computer Simulation Models, Neurological Action Potentials Animals
View on PubMed DOI
Popular Topics
Recent Journals