AIMC Topic: Neurons

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Neuromorphic computing at scale.

Nature
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...

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)
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...

Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons.

IEEE transactions on bio-medical engineering
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.

Towards parameter-free attentional spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
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...

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
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 mechanisms of relational learning and fast knowledge reassembly in plastic neural networks.

Nature neuroscience
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...

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing.

ACS nano
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magne...

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

Nano letters
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...

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

IEEE transactions on neural networks and learning systems
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...

Acquisition of similar properties by filters in the same stream of a multistream convolutional neural network.

Scientific reports
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...