AIMC Topic: Action Potentials

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RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.

Neuroinformatics Jan 4, 2021
Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and beh...
Artificial Intelligence Action Potentials Hippocampus Neural Networks, Computer
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Constraints on Hebbian and STDP learned weights of a spiking neuron.

Neural networks : the official journal of the International Neural Network Society Jan 2, 2021
We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbian learning, we find that the normalised weights equal the promotion p...
Neuronal Plasticity Humans Models, Neurological Algorithms Neurons Action Potentials Deep Learning
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Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise.

PloS one Dec 31, 2020
With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system ha...
Action Potentials Neurons Neuronal Plasticity Synapses Neural Networks, Computer Models, Neurological Algorithms
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A simple Ca-imaging approach to neural network analyses in cultured neurons.

Journal of neuroscience methods Dec 17, 2020
BACKGROUND: Ca-imaging is a powerful tool to measure neuronal dynamics and network activity. To monitor network-level changes in cultured neurons, neuronal activity is often evoked by electrical or optogenetic stimulation and assessed using multi-ele...
Neural Networks, Computer Animals Mice Action Potentials Algorithms Cells, Cultured Neurons Optogenetics
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A generative spiking neural-network model of goal-directed behaviour and one-step planning.

PLoS computational biology Dec 8, 2020
In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models...
Brain Goals Action Potentials Neural Networks, Computer Humans Bayes Theorem Algorithms
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A Novel Neural Model With Lateral Interaction for Learning Tasks.

Neural computation Nov 30, 2020
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some n...
Algorithms Learning Models, Neurological Action Potentials Neuronal Plasticity Humans Neural Networks, Computer Neurons
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Conductance-Based Adaptive Exponential Integrate-and-Fire Model.

Neural computation Nov 30, 2020
The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integ...
Action Potentials Adaptation, Physiological Neural Networks, Computer Humans Animals Models, Neurological
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Inferring a network from dynamical signals at its nodes.

PLoS computational biology Nov 30, 2020
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they ...
Neurons Proof of Concept Study Brain Gene Regulatory Networks Action Potentials Neural Networks, Computer Algorithms
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Deep-learned spike representations and sorting via an ensemble of auto-encoders.

Neural networks : the official journal of the International Neural Network Society Nov 27, 2020
Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the...
Action Potentials Signal Processing, Computer-Assisted Neurons Databases, Factual Machine Learning Deep Learning Algorithms
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Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

Circulation. Arrhythmia and electrophysiology Nov 13, 2020
BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiogr...
Atrial Fibrillation Heart Rate Signal Processing, Computer-Assisted Heart Conduction System Risk Assessment Incidence Action Potentials Predictive Value of Tests Electrocardiography Aged, 80 and over Male Risk Factors Aged Machine Learning Female Humans
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