AIMC Topic: Neurons

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A Model for Structured Information Representation in Neural Networks of the Brain.

eNeuro May 29, 2020
Humans can reason at an abstract level and structure information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the br...
Humans Models, Neurological Neurons Neural Networks, Computer Brain
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Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.

International journal of neural systems May 28, 2020
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carr...
Humans Supervised Machine Learning Membrane Potentials Neural Networks, Computer Neurons Models, Neurological
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A Modeling Study of the Emergence of Eye Position Gain Fields Modulating the Responses of Visual Neurons in the Brain.

Frontiers in neural circuits May 26, 2020
The responses of many cortical neurons to visual stimuli are modulated by the position of the eye. This form of gain modulation by eye position does not change the retinotopic selectivity of the responses, but only changes the amplitude of the respon...
Visual Cortex Neurons Visual Perception Visual Fields Humans Neural Networks, Computer Normal Distribution Eye Movements Photic Stimulation
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Generation of Scale-Invariant Sequential Activity in Linear Recurrent Networks.

Neural computation May 20, 2020
Sequential neural activity has been observed in many parts of the brain and has been proposed as a neural mechanism for memory. The natural world expresses temporal relationships at a wide range of scales. Because we cannot know the relevant scales a...
Animals Rats Exercise Test Memory Neurons Linear Models Neural Networks, Computer Brain
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Minimal Spiking Neuron for Solving Multilabel Classification Tasks.

Neural computation May 20, 2020
The multispike tempotron (MST) is a powersul, single spiking neuron model that can solve complex supervised classification tasks. It is also internally complex, computationally expensive to evaluate, and unsuitable for neuromorphic hardware. Here we ...
Humans Animals Neurons Deep Learning Action Potentials
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Entropy, mutual information, and systematic measures of structured spiking neural networks.

Journal of theoretical biology May 19, 2020
The aim of this paper is to investigate various information-theoretic measures, including entropy, mutual information, and some systematic measures that are based on mutual information, for a class of structured spiking neuronal networks. In order to...
Neurons Entropy Neural Networks, Computer
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Memristor networks for real-time neural activity analysis.

Nature communications May 15, 2020
The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and sto...
Electricity Neurons Neural Networks, Computer Action Potentials
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Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

Scientific reports May 15, 2020
Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the con...
Humans Brain Action Potentials Neural Networks, Computer Algorithms Pattern Recognition, Automated Memory Neurons Supervised Machine Learning Synapses
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Analog neuron hierarchy.

Neural networks : the official journal of the International Neural Network Society May 11, 2020
In order to refine the analysis of the computational power of discrete-time recurrent neural networks (NNs) between the binary-state NNs which are equivalent to finite automata (level 3 in the Chomsky hierarchy), and the analog-state NNs with rationa...
Neurons Neural Networks, Computer Language
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Training memristor-based multilayer neuromorphic networks with SGD, momentum and adaptive learning rates.

Neural networks : the official journal of the International Neural Network Society May 7, 2020
Neural networks implemented with traditional hardware face inherent limitation of memory latency. Specifically, the processing units like GPUs, FPGAs, and customized ASICs, must wait for inputs to read from memory and outputs to write back. This moti...
Humans Neural Networks, Computer Learning Neurons Motion
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