AIMC Topic: Action Potentials

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Recapitulating the electrophysiological features of in vivo biological networks by using a real-time hardware Spiking Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electroceutical methodologies utilized for treating neurological disorders, including stroke, can leverage neuromorphic engineering principles to design devices capable of seamlessly interfacing with the neural system. This paper introduces a bank of...
Action Potentials Neurons Nerve Net Neural Networks, Computer Animals Rats
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Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

Neural computation Jun 7, 2024
The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for t...
Brain Neuronal Plasticity Models, Neurological Humans Neural Networks, Computer Action Potentials Unsupervised Machine Learning Neurons
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A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Neural computation Jun 7, 2024
Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of ...
Humans Neural Networks, Computer Computer Simulation Nerve Net Animals Models, Neurological Action Potentials Neurons Synapses
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Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

International journal of neural systems Jun 1, 2024
Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original S...
Adenosine Triphosphate Synapses Synaptic Transmission Humans Astrocytes Neural Networks, Computer Neurons Time Factors Models, Neurological Action Potentials
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Validation of a machine learning algorithm to identify pulmonary vein isolation during ablation procedures for the treatment of atrial fibrillation: results of the PVISION study.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology May 2, 2024
AIMS: Pulmonary vein isolation (PVI) is the cornerstone of ablation for atrial fibrillation. Confirmation of PVI can be challenging due to the presence of far-field electrograms (EGMs) and sometimes requires additional pacing manoeuvres or mapping. T...
Atrial Fibrillation Heart Rate Catheter Ablation Pulmonary Veins Electrophysiologic Techniques, Cardiac Algorithms Action Potentials Predictive Value of Tests Prospective Studies Signal Processing, Computer-Assisted Female Middle Aged Treatment Outcome Humans Reproducibility of Results Machine Learning Aged Male
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Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks.

Neural computation Apr 23, 2024
Deep feedforward and recurrent neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale's law. Here we argue that these details are crucial in order to understand how r...
Synapses Nerve Net Humans Neural Inhibition Neural Networks, Computer Action Potentials Computer Simulation Animals Neurons Nonlinear Dynamics Models, Neurological
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A robust balancing mechanism for spiking neural networks.

Chaos (Woodbury, N.Y.) Apr 1, 2024
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the ...
Synapses Neural Networks, Computer Action Potentials Neurons Neuronal Plasticity Models, Neurological
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Noisy image segmentation based on synchronous dynamics of coupled photonic spiking neurons.

Optics express Oct 23, 2023
The collective dynamics in neural networks is essential for information processing and has attracted much interest on the application in artificial intelligence. Synchronization is one of the most dominant phenomenon in the collective dynamics of neu...
Artificial Intelligence Models, Neurological Neural Networks, Computer Neurons Action Potentials Optics and Photonics Photons
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Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.

Neural computation Aug 7, 2023
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is r...
Neurons Neuronal Plasticity Neural Networks, Computer Models, Neurological Action Potentials
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An efficient deep learning approach to identify dynamics in in vitro neural networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity...
Action Potentials Algorithms Humans Deep Learning Neural Networks, Computer Models, Neurological
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