AIMC Topic: Action Potentials

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Third-order nanocircuit elements for neuromorphic engineering.

Nature Sep 23, 2020
Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that nat...
Electrophysiology Engineering Models, Neurological Action Potentials Electrodes Biomimetics Logic Artificial Intelligence Computer Simulation
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Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

PloS one Sep 23, 2020
In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibilit...
Neuronal Plasticity Neural Networks, Computer Models, Neurological Synaptic Transmission Algorithms Action Potentials Models, Statistical Deep Learning Computer Simulation Neurons Models, Theoretical Learning Synapses
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Learning probabilistic neural representations with randomly connected circuits.

Proceedings of the National Academy of Sciences of the United States of America Sep 18, 2020
The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is th...
Humans Neural Networks, Computer Machine Learning Models, Neurological Action Potentials Neurons Algorithms Brain Neuronal Plasticity Nerve Net
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Turing Universality of Weighted Spiking Neural P Systems with Anti-spikes.

Computational intelligence and neuroscience Sep 17, 2020
Weighted spiking neural P systems with anti-spikes (AWSN P systems) are proposed by adding anti-spikes to spiking neural P systems with weighted synapses. Anti-spikes behave like spikes of inhibition of communication between neurons. Both spikes and ...
Synapses Neural Networks, Computer Action Potentials Models, Neurological Neurons Biomimetics
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Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

Circulation. Arrhythmia and electrophysiology Sep 13, 2020
BACKGROUND: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypoth...
Electrophysiologic Techniques, Cardiac Voltage-Sensitive Dye Imaging Time Factors Machine Learning Action Potentials Humans Atrial Fibrillation Reproducibility of Results Predictive Value of Tests Heart Rate Spectroscopy, Near-Infrared Fourier Analysis
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Systematic errors in connectivity inferred from activity in strongly recurrent networks.

Nature neuroscience Sep 7, 2020
Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them...
Humans Neural Networks, Computer Data Analysis Action Potentials Neurons Neural Pathways Brain Animals Models, Neurological
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Spiking Neural P Systems with Extended Channel Rules.

International journal of neural systems Aug 18, 2020
This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP-ECR systems). SNP-ECR systems are a class of distributed parallel computing models. In SNP-ECR...
Neural Networks, Computer Models, Neurological Action Potentials Neurons Synapses
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Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.

Neural networks : the official journal of the International Neural Network Society Aug 17, 2020
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Spiking neural networks (SNNs) represent a family of event-driven models...
Vision, Ocular Humans Recognition, Psychology Neural Networks, Computer Databases, Factual Machine Learning Action Potentials Algorithms Neurons
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Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Nature communications Aug 12, 2020
Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphe...
Signal Processing, Computer-Assisted Transistors, Electronic Logic Nociception Neuronal Plasticity Presynaptic Terminals Action Potentials Robotics
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SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons.

Scientific reports Aug 4, 2020
There has been a lot of research on supervised learning in spiking neural network (SNN) for a couple of decades to improve computational efficiency. However, evolutionary algorithm based supervised learning for SNN has not been investigated thoroughl...
Nerve Net Supervised Machine Learning Neurons Action Potentials
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