AIMC Topic: Action Potentials

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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.

Neural networks : the official journal of the International Neural Network Society Sep 27, 2019
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...
Action Potentials Supervised Machine Learning Pattern Recognition, Automated Neurons Humans Neural Networks, Computer Neuronal Plasticity
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Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence.

Neural networks : the official journal of the International Neural Network Society Sep 26, 2019
Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabil...
Neurons Brain Humans Computer Systems Neural Networks, Computer Action Potentials Artificial Intelligence Machine Learning Computers
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Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE).

Neural networks : the official journal of the International Neural Network Society Sep 25, 2019
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in...
Humans Neural Networks, Computer Reinforcement, Psychology Memory, Short-Term Neurons Learning Mental Recall Prefrontal Cortex Action Potentials
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Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform.

Neural networks : the official journal of the International Neural Network Society Sep 24, 2019
Neural networks have enabled great advances in recent times due mainly to improved parallel computing capabilities in accordance to Moore's Law, which allowed reducing the time needed for the parameter learning of complex, multi-layered neural archit...
Humans Neural Networks, Computer Supervised Machine Learning Recognition, Psychology Neurons Biomimetics Algorithms Brain Action Potentials
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Using intersection information to map stimulus information transfer within neural networks.

Bio Systems Sep 21, 2019
Analytical tools that estimate the directed information flow between simultaneously recorded neural populations, such as directed information or Granger causality, typically focus on measuring how much information is exchanged between such population...
Information Theory Models, Neurological Action Potentials Neurons Nerve Net Electric Stimulation Synaptic Transmission Neural Networks, Computer Algorithms Animals
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Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.

Neural networks : the official journal of the International Neural Network Society Sep 20, 2019
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...
Action Potentials Electroencephalography Brain-Computer Interfaces Humans Neural Networks, Computer Brain Deep Learning Cognition
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Rethinking the performance comparison between SNNS and ANNS.

Neural networks : the official journal of the International Neural Network Society Sep 19, 2019
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced remarkable success via mature models, various benchmarks, open-source datasets, and powerful computing platforms. Spiking neural networks (SNNs), a ca...
Action Potentials Neurons Pattern Recognition, Automated Memory Artificial Intelligence Algorithms Humans Brain Neural Networks, Computer
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Spiking Neural Networks and online learning: An overview and perspectives.

Neural networks : the official journal of the International Neural Network Society Sep 7, 2019
Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they...
Algorithms Brain Models, Neurological Humans Neural Networks, Computer Action Potentials Neurons Education, Distance
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Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes Sep 5, 2019
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...
Humans Neural Networks, Computer Machine Learning Markov Chains Pattern Recognition, Automated Electrocardiography Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted Workflow Heart Rate Databases, Factual Prognosis Reproducibility of Results Cardiovascular Diseases Diagnosis, Computer-Assisted Time Factors
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Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice.

eNeuro Sep 5, 2019
Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity during seizures is only one extreme. The aim of this study was to differentiate between different t...
Animals Male Mice Epilepsy Machine Learning Action Potentials Hippocampus Mice, Inbred C57BL Kainic Acid Electroencephalography
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