AIMC Topic: Action Potentials

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Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The Journal of neuroscience : the official journal of the Society for Neuroscience Jul 3, 2019
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from as...
Neural Networks, Computer Synapses Memory Conditioning, Classical Association Learning Models, Neurological Action Potentials Neurons
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Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network.

IEEE transactions on biomedical circuits and systems Jun 27, 2019
An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associate...
Movement Motor Neurons Signal Processing, Computer-Assisted Hand Electromyography Humans Neural Networks, Computer Gestures Models, Neurological Action Potentials Muscle, Skeletal
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Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes.

IEEE transactions on neural networks and learning systems Jun 21, 2019
Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propos...
Humans Cognition Action Potentials Neurons Neural Networks, Computer Machine Learning Algorithms Psychomotor Performance
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Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method.

Journal of neurophysiology Jun 19, 2019
Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can...
Neural Networks, Computer Motor Neurons Astacoidea Algorithms Models, Biological Animals Nerve Net Action Potentials Sensory Receptor Cells
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A Probabilistic Synapse With Strained MTJs for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Jun 18, 2019
Spiking neural networks (SNNs) are of interest for applications for which conventional computing suffers from the nearly insurmountable memory-processor bottleneck. This paper presents a stochastic SNN architecture that is based on specialized logic-...
Probability Synapses Humans Machine Learning Neural Networks, Computer Neuronal Plasticity Action Potentials
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All-optical spiking neurosynaptic networks with self-learning capabilities.

Nature May 8, 2019
Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computi...
Pattern Recognition, Automated Nerve Net Photons Neurons Biomimetics Synapses Computers Neural Networks, Computer Computer Systems Action Potentials Models, Neurological Unsupervised Machine Learning Supervised Machine Learning
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Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Apr 12, 2019
Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provide...
Signal-To-Noise Ratio Models, Neurological Neural Networks, Computer Action Potentials Algorithms Signal Processing, Computer-Assisted Reproducibility of Results
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Microstimulation in a spiking neural network model of the midbrain superior colliculus.

PLoS computational biology Apr 12, 2019
The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the...
Superior Colliculi Haplorhini Electric Stimulation Saccades Action Potentials Animals Neurons Computational Biology Nerve Net Models, Neurological Humans Neural Networks, Computer
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Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance.

IEEE transactions on neural networks and learning systems Apr 11, 2019
We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 7...
Pattern Recognition, Automated Humans Cerebral Cortex Neural Networks, Computer Electroencephalography Phase Synchronization Machine Learning Alpha Rhythm Action Potentials Algorithms Astrocytes Computer Simulation Neurons
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An unsupervised neuromorphic clustering algorithm.

Biological cybernetics Apr 3, 2019
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, hi...
Humans Neural Networks, Computer Algorithms Cluster Analysis Action Potentials Photic Stimulation Unsupervised Machine Learning Pattern Recognition, Visual Neurons Pattern Recognition, Automated
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