AIMC Topic: Neurons

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Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean.

Neural networks : the official journal of the International Neural Network Society Jun 7, 2022
The vanishing gradient problem (i.e., gradients prematurely becoming extremely small during training, thereby effectively preventing a network from learning) is a long-standing obstacle to the training of deep neural networks using sigmoid activation...
Neural Networks, Computer Algorithms Models, Theoretical Neurons
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A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Jun 6, 2022
This paper describes a fully experimental hybrid system in which a [Formula: see text] memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180 nm CMOS t...
Neural Networks, Computer Neurons
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NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size.

IEEE transactions on pattern analysis and machine intelligence Jun 3, 2022
Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Architecture topology and architecture size have been regarded as two of th...
Neural Networks, Computer Algorithms Plant Extracts Neurons
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Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing.

Nature communications Jun 3, 2022
Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brai...
Humans Neural Networks, Computer Brain Reproducibility of Results Neurons
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A Novel Adaptive Linear Neuron Based on DNA Strand Displacement Reaction Network.

IEEE/ACM transactions on computational biology and bioinformatics Jun 3, 2022
Analog DNA strand displacement circuits can be used to build artificial neural network due to the continuity of dynamic behavior. In this study, DNA implementations of novel catalysis, novel degradation and adjustment reaction modules are designed an...
Neural Networks, Computer Algorithms Computer Simulation DNA Neurons
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Lag Synchronization of Noisy and Nonnoisy Multiple Neurobiological Coupled FitzHugh-Nagumo Networks with and without Delayed Coupling.

Computational intelligence and neuroscience Jun 2, 2022
This paper presents a methodology for synchronizing noisy and nonnoisy multiple coupled neurobiological FitzHugh-Nagumo (FHN) drive and slave neural networks with and without delayed coupling, under external electrical stimulation (EES), external dis...
Neural Networks, Computer Brain Models, Neurological Neurons Electric Stimulation
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Printed synaptic transistor-based electronic skin for robots to feel and learn.

Science robotics Jun 1, 2022
An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices...
Humans Wearable Electronic Devices Robotics Neurons Electronics Conditioning, Classical
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The geometry of robustness in spiking neural networks.

eLife May 30, 2022
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional r...
Neural Networks, Computer Models, Neurological Action Potentials Neurons
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Emergence of associative learning in a neuromorphic inference network.

Journal of neural engineering May 30, 2022
. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of...
Neural Networks, Computer Artificial Intelligence Bayes Theorem Neurons Synapses
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Programming Molecular Systems To Emulate a Learning Spiking Neuron.

ACS synthetic biology May 27, 2022
Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts ...
Neural Networks, Computer Brain Learning Neurons
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