AIMC Topic: Action Potentials

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Robust Transcoding Sensory Information With Neural Spikes.

IEEE transactions on neural networks and learning systems
Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies ...

Dynamic Instability and Time Domain Response of a Model Halide Perovskite Memristor for Artificial Neurons.

The journal of physical chemistry letters
Memristors are candidate devices for constructing artificial neurons, synapses, and computational networks for brainlike information processing and sensory-motor autonomous systems. However, the dynamics of natural neurons and synapses are challengin...

A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory.

Network (Bristol, England)
This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and...

Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models.

Scientific reports
Isolated spikes and bursts of spikes are thought to provide the two major modes of information coding by neurons. Bursts are known to be crucial for fundamental processes between neuron pairs, such as neuronal communications and synaptic plasticity. ...

Synaptic Learning With Augmented Spikes.

IEEE transactions on neural networks and learning systems
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements ...

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while ...

The effects of temperature on the dynamics of the biological neural network.

Journal of biological physics
The nerve cells are responsible for transmitting messages through the action potential, which generates electrical stimulation. One of the methods and tools of electrical stimulation is infrared neural stimulation (INS). Since the mechanism of INS is...

Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters.

Small (Weinheim an der Bergstrasse, Germany)
One of the important steps for realizing artificial intelligence is identifying elementary units that are beneficial for neural network construction. A type of memristive behavior in which phase-change nanoclusters nucleate adaptively in two adjacent...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BMC cardiovascular disorders
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...

Synaptic Weight Evolution and Charge Trapping Mechanisms in a Synaptic Pass-Transistor Operation With a Direct Potential Output.

IEEE transactions on neural networks and learning systems
We present an intensive study on the weight modulation and charge trapping mechanisms of the synaptic transistor based on a pass-transistor concept for the direct voltage output. In this article, the pass-transistor concept for a metal-oxide-semicond...