AIMC Topic: Action Potentials

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Multiple machine learning methods aided virtual screening of Na 1.5 inhibitors.

Journal of cellular and molecular medicine
Na 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion chan...

A deep learning platform to assess drug proarrhythmia risk.

Cell stem cell
Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinica...

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

PLoS computational biology
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocamp...

A MoS Hafnium Oxide Based Ferroelectric Encoder for Temporal-Efficient Spiking Neural Network.

Advanced materials (Deerfield Beach, Fla.)
Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applicatio...

Recognizing intertwined patterns using a network of spiking pattern recognition platforms.

Scientific reports
Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making...

Computational modeling of color perception with biologically plausible spiking neural networks.

PLoS computational biology
Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (...

Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane Controllers.

International journal of neural systems
Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rew...

Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.

IEEE transactions on neural networks and learning systems
Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an en...

Spiking Neural P Systems With Enzymes.

IEEE transactions on nanobioscience
The neurotransmitter is a chemical substance that transmits information between neurons. Its metabolic process includes four links: synthesis, storage, release and inactivation. As one of the important chemical components of neurotransmitters, acetyl...

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI.

Sensors (Basel, Switzerland)
Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for powe...