AIMC Topic: Action Potentials

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Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

IEEE transactions on bio-medical engineering Jan 19, 2024
BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrim...
Animals Rats Neural Networks, Computer Electrodes Sciatic Nerve Action Potentials Prostheses and Implants
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Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation.

IEEE transactions on bio-medical engineering Dec 22, 2023
OBJECTIVE: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth kno...
Electrophysiologic Techniques, Cardiac Deep Learning Action Potentials Rabbits Atrial Fibrillation Artificial Intelligence Animals
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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 Neurons Cerebral Cortex Neural Networks, Computer Action Potentials Membrane Potentials
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Supervised Learning in Multilayer Spiking Neural Networks With Spike Temporal Error Backpropagation.

IEEE transactions on neural networks and learning systems Nov 30, 2023
The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power consumption and powerful computing capability. However, the lack of effective learning algorithms has obstructed the theoretical advance and applications of SNNs. Th...
Supervised Machine Learning Action Potentials Neural Networks, Computer Algorithms Brain
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Beyond spiking networks: The computational advantages of dendritic amplification and input segregation.

Proceedings of the National Academy of Sciences of the United States of America Nov 29, 2023
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve st...
Artificial Intelligence Neurons Models, Neurological Machine Learning Brain Action Potentials
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Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Nov 3, 2023
OBJECTIVE: To develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG) recordings.
Muscles Electromyography Muscle Contraction Muscle, Skeletal Humans Artificial Intelligence Action Potentials
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Learning heterogeneous delays in a layer of spiking neurons for fast motion detection.

Biological cybernetics Sep 11, 2023
The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiolo...
Learning Neural Networks, Computer Neurons Algorithms Action Potentials
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Spiking neural P systems with lateral inhibition.

Neural networks : the official journal of the International Neural Network Society Aug 12, 2023
As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in recent years. This work introduces the phenomenon of lateral inhibition in biological nervous systems ...
Neurons Action Potentials Neural Networks, Computer Models, Neurological
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Long- and short-term history effects in a spiking network model of statistical learning.

Scientific reports Aug 9, 2023
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability d...
Action Potentials Neural Networks, Computer Algorithms Learning Brain Models, Neurological
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Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition.

Neural networks : the official journal of the International Neural Network Society Jul 20, 2023
Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally s...
Recognition, Psychology Time Perception Neural Networks, Computer Action Potentials Neurons
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