AIMC Topic: Membrane Potentials

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Multitask learning of a biophysically-detailed neuron model.

PLoS computational biology Jul 31, 2024
The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective meth...
Computational Biology Models, Neurological Brain Computer Simulation Neurons Humans Magnetoencephalography Neural Networks, Computer Membrane Potentials Machine Learning Action Potentials Electroencephalography
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Neural network aided extended Kalman filtering for inverse imaging of cardiac transmembrane potential.

Physics in medicine and biology Jun 26, 2024
The aim of this study is to address the limitations in reconstructing the electrical activity of the heart from the body surface electrocardiogram, which is an ill-posed inverse problem. Current methods often assume values commonly used in the litera...
Humans Image Processing, Computer-Assisted Heart Neural Networks, Computer Animals Membrane Potentials
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A thermodynamical model of non-deterministic computation in cortical neural networks.

Physical biology Dec 11, 2023
Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic natur...
Humans Neural Networks, Computer Cerebral Cortex Membrane Potentials Action Potentials Neurons
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Energy-efficiency computing of up and down transitions in a neural network.

Journal of neurophysiology Feb 1, 2023
Spontaneous periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity of slow-wave sleep. Previous theoretical studies have shown that stimulation frequency and the dynamics of intrinsic currents ...
Neurons Membrane Potentials Neural Networks, Computer Models, Neurological Action Potentials
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SPIDE: A purely spike-based method for training feedback spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 24, 2023
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural netwo...
Membrane Potentials Feedback Action Potentials Neural Networks, Computer Computers
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BackEISNN: A deep spiking neural network with adaptive self-feedback and balanced excitatory-inhibitory neurons.

Neural networks : the official journal of the International Neural Network Society Jul 11, 2022
Spiking neural networks (SNNs) transmit information through discrete spikes that perform well in processing spatial-temporal information. Owing to their nondifferentiable characteristic, difficulties persist in designing SNNs that deliver good perfor...
Neural Networks, Computer Neurons Feedback Models, Neurological Membrane Potentials
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Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease.

PloS one Feb 24, 2022
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower effic...
Machine Learning Female Humans Neural Networks, Computer Membrane Potentials Male Deep Learning Electroencephalography Aged Parkinson Disease Middle Aged Wavelet Analysis Mental Health
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Zero-Hopf Bifurcation of a memristive synaptic Hopfield neural network with time delay.

Neural networks : the official journal of the International Neural Network Society Feb 18, 2022
This paper proposes a novel memristive synaptic Hopfield neural network (MHNN) with time delay by using a memristor synapse to simulate the electromagnetic induced current caused by the membrane potential difference between two adjacent neurons. Firs...
Neurons Neural Networks, Computer Membrane Potentials
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A Frequency Domain Analysis of the Excitability and Bifurcations of the FitzHugh-Nagumo Neuron Model.

The journal of physical chemistry letters Nov 5, 2021
The dynamics of neurons consist of oscillating patterns of a membrane potential that underpin the operation of biological intelligence. The FitzHugh-Nagumo (FHN) model for neuron excitability generates rich dynamical regimes with a simpler mathematic...
Dielectric Spectroscopy Membrane Potentials Neural Networks, Computer Models, Neurological Neurons
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A simple parametric representation of the Hodgkin-Huxley model.

PloS one Jul 22, 2021
The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane ...
Axons Models, Neurological Decapodiformes Machine Learning Membrane Potentials Animals Action Potentials
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