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Action Potentials

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An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

IEEE transactions on neural networks and learning systems
Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional mod...

Measuring predictability of autonomous network transitions into bursting dynamics.

PloS one
Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-...

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

IEEE transactions on neural networks and learning systems
Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, ...

Regulation of Local Ambient GABA Levels via Transporter-Mediated GABA Import and Export for Subliminal Learning.

Neural computation
Perception of supraliminal stimuli might in general be reflected in bursts of action potentials (spikes), and their memory traces could be formed through spike-timing-dependent plasticity (STDP). Memory traces for subliminal stimuli might be formed i...

Surrogate population models for large-scale neural simulations.

Neural computation
Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to m...

Granger causality-based synaptic weights estimation for analyzing neuronal networks.

Journal of computational neuroscience
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC ...

Hardware-amenable structural learning for spike-based pattern classification using a simple model of active dendrites.

Neural computation
This letter presents a spike-based model that employs neurons with functionally distinct dendritic compartments for classifying high-dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before ...

Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity.

Nano letters
Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow ...

Designing responsive pattern generators: stable heteroclinic channel cycles for modeling and control.

Bioinspiration & biomimetics
A striking feature of biological pattern generators is their ability to respond immediately to multisensory perturbations by modulating the dwell time at a particular phase of oscillation, which can vary force output, range of motion, or other charac...

Bayes optimal template matching for spike sorting - combining fisher discriminant analysis with optimal filtering.

Journal of computational neuroscience
Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the d...