AIMC Topic: Neurons

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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.

PLoS computational biology Jan 29, 2025
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...
Calcium Synapses Animals Machine Learning Computational Biology Learning Neuronal Plasticity Models, Neurological Humans Neurons Neural Networks, Computer
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A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.

Nature methods Jan 27, 2025
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...
Microscopy, Fluorescence Algorithms Neurons Brain Animals Deep Learning Image Processing, Computer-Assisted Mice Imaging, Three-Dimensional Brain Mapping
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Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor.

PloS one Jan 27, 2025
Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. Rece...
Neurons Synapses Transistors, Electronic Neural Networks, Computer Models, Neurological Action Potentials
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Self-supervised learning of scale-invariant neural representations of space and time.

Journal of computational neuroscience Jan 22, 2025
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in...
Space Perception Time Perception Neurons Hippocampus Neural Networks, Computer Learning Models, Neurological Supervised Machine Learning Animals
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Neuromorphic computing at scale.

Nature Jan 22, 2025
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficien...
Brain Humans Neural Networks, Computer Animals Neurons Algorithms Computers
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Real-Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device.

Small (Weinheim an der Bergstrasse, Germany) Jan 21, 2025
Spiking neurons are essential for building energy-efficient biomimetic spatiotemporal systems because they communicate with other neurons using sparse and binary signals. However, the achievable high density of artificial neurons having a capacitor f...
Neural Networks, Computer Unsupervised Machine Learning Neurons Humans
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Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice.
Robotics Equipment Design Neurons Animals Mice Patch-Clamp Techniques
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Towards parameter-free attentional spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 16, 2025
Brain-inspired spiking neural networks (SNNs) are increasingly explored for their potential in spatiotemporal information modeling and energy efficiency on emerging neuromorphic hardware. Recent works incorporate attentional modules into SNNs, greatl...
Attention Neural Networks, Computer Brain Action Potentials Neurons Models, Neurological Humans
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Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.

Proceedings of the National Academy of Sciences of the United States of America Jan 16, 2025
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information main...
Neural Networks, Computer Animals Prefrontal Cortex Humans Memory, Short-Term Neurons Synapses Cognition Models, Neurological Nerve Net
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Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks.

Nature neuroscience Jan 15, 2025
Humans and animals have a striking ability to learn relationships between items in experience (such as stimuli, objects and events), enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational l...
Neuronal Plasticity Neural Networks, Computer Models, Neurological Learning Nerve Net Humans Neurons Animals Knowledge
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