AIMC Topic: Action Potentials

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Neuromorphic meets neuromechanics, part I: the methodology and implementation.

Journal of neural engineering Jan 13, 2017
OBJECTIVE: One goal of neuromorphic engineering is to create 'realistic' robotic systems that interact with the physical world by adopting neuromechanical principles from biology. Critical to this is the methodology to implement the spinal circuitry ...
Robotics Signal Processing, Computer-Assisted Biomimetics Models, Neurological Action Potentials Muscle, Skeletal Afferent Pathways Humans Muscle Spindles Computer Simulation Neuromuscular Junction Animals Muscle Contraction Synaptic Transmission Motor Neurons
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Comparison of Classifier Architectures for Online Neural Spike Sorting.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Dec 19, 2016
High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce...
Computer Simulation Reproducibility of Results Sensitivity and Specificity Humans Machine Learning Pattern Recognition, Automated Nerve Net Online Systems Action Potentials Animals Models, Statistical Computer Systems Neurons Models, Neurological
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Robust learning in SpikeProp.

Neural networks : the official journal of the International Neural Network Society Nov 8, 2016
Training a Spiking Neural Network using SpikeProp and its derivatives faces stability issues. Surges, marked by a sudden rise in learning cost, are a common occurrence during the learning process. They disrupt the learning process and often destabili...
Humans Neural Networks, Computer Machine Learning Algorithms Action Potentials
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Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

Journal of computational neuroscience Nov 4, 2016
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the unde...
Models, Neurological Action Potentials Neurons Neural Networks, Computer Learning Neuronal Plasticity Nerve Net
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Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PLoS computational biology Oct 19, 2016
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combine...
Biological Clocks Pattern Recognition, Automated Nerve Net Pattern Recognition, Physiological Unsupervised Machine Learning Neurons Neural Networks, Computer Computer Simulation Neuronal Plasticity Animals Humans Models, Neurological Action Potentials
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Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

Bio Systems Sep 22, 2016
In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitio...
Neuronal Plasticity Action Potentials Nerve Net Humans Cortical Synchronization Neural Networks, Computer
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Spiking Neural P Systems with Neuron Division and Dissolution.

PloS one Sep 14, 2016
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational ...
Humans Neural Networks, Computer Computer Simulation Action Potentials Neurons
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Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks.

Journal of computational neuroscience Sep 2, 2016
A large body of experimental and theoretical work on neural coding suggests that the information stored in brain circuits is represented by time-varying patterns of neural activity. Reservoir computing, where the activity of a recurrently connected p...
Humans Neural Networks, Computer Brain Computer Simulation Animals Models, Neurological Nonlinear Dynamics Action Potentials Synapses Neurons
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A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

International journal of neural systems Sep 1, 2016
This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timi...
Neural Inhibition Synapses Cerebral Cortex Wavelet Analysis Time Factors Action Potentials Supervised Machine Learning Humans Epilepsy Neurons Neural Networks, Computer
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Global oscillation regime change by gated inhibition.

Neural networks : the official journal of the International Neural Network Society Jul 18, 2016
The role of sensory inputs in the modelling of synchrony regimes is exhibited by means of networks of spiking cells where the relative strength of the inhibitory interaction is controlled by the activation of a linear unit working as a gating variabl...
Biological Clocks Neural Networks, Computer Animals Action Potentials Neurons Attention Neural Inhibition
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