AIMC Topic: Neurons

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Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses.

Nano letters Jan 13, 2025
Multifunctional hardware technologies for neuromorphic computing are essential for replicating the complexity of biological neural systems, thereby improving the performance of artificial synapses and neurons. Integrating ionic and spintronic technol...
Synapses Magnetic Fields Neural Networks, Computer Neurons
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Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

IEEE transactions on neural networks and learning systems Jan 7, 2025
Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific...
Animals Models, Neurological Neural Networks, Computer Neurons Computer Simulation Humans Nerve Net Action Potentials Memory Astrocytes
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Acquisition of similar properties by filters in the same stream of a multistream convolutional neural network.

Scientific reports Jan 2, 2025
Functional modular organization is observed in a variety of cortical areas in the brain. In the visual cortex of primates, adjacent neurons often respond to the same visual submodality, such as color or orientation, and have a similar preferred orien...
Neural Networks, Computer Photic Stimulation Visual Cortex Animals Neurons
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Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.

Nature communications Jan 2, 2025
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited re...
Humans Photic Stimulation Visual Cortex Neurons Visual Pathways Orientation Neural Networks, Computer Models, Neurological Vision, Ocular
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CS-QCFS: Bridging the performance gap in ultra-low latency spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 1, 2025
Spiking Neural Networks (SNNs) are at the forefront of computational neuroscience, emulating the nuanced dynamics of biological systems. In the realm of SNN training methods, the conversion from ANNs to SNNs has generated significant interest due to ...
Neurons Models, Neurological Action Potentials Neural Networks, Computer Algorithms Humans
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Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Dec 31, 2024
The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell...
Neurons Alzheimer Disease Patch-Clamp Techniques Action Potentials Humans Ion Channels Kinetics Deep Learning
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A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.

BMC neurology Dec 30, 2024
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high e...
Models, Neurological Parkinson Disease Humans Neural Networks, Computer Action Potentials Neurons Algorithms
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Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.

Neural networks : the official journal of the International Neural Network Society Dec 26, 2024
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy...
Optogenetics Neural Networks, Computer Algorithms Neurons Animals Computer Simulation Nerve Net Models, Neurological
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Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios.

International journal of neural systems Dec 23, 2024
In the last few decades, Artificial Neural Networks have become more and more important, evolving into a powerful tool to implement learning algorithms. Spiking neural networks represent the third generation of Artificial Neural Networks; they have e...
Epilepsy Humans Neurons Algorithms Action Potentials Neural Networks, Computer
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Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

Neural networks : the official journal of the International Neural Network Society Dec 19, 2024
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...
Humans Reinforcement, Psychology Computer Simulation Neural Networks, Computer Neuronal Plasticity Deep Learning Action Potentials Models, Neurological Neurons
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