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Action Potentials

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Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.

Sensors (Basel, Switzerland)
Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs ...

A study of autoencoders as a feature extraction technique for spike sorting.

PloS one
Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, cur...

Edge computing on TPU for brain implant signal analysis.

Neural networks : the official journal of the International Neural Network Society
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision off...

Energy-efficiency computing of up and down transitions in a neural network.

Journal of neurophysiology
Spontaneous periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity of slow-wave sleep. Previous theoretical studies have shown that stimulation frequency and the dynamics of intrinsic currents ...

SPIDE: A purely spike-based method for training feedback spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural netwo...

The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks.

Neuron
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, comput...

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of prec...

Asynchronous Spiking Neural P Systems With Rules Working in the Rule Synchronization Mode.

IEEE transactions on nanobioscience
Asynchronous spiking neural P systems with rules on synapses (ARSSN P systems) are a class of computation models, where spiking rules are placed on synapses. In this work, we investigate the computation power of ARSSN P systems working in the rule sy...

Multiple machine learning methods aided virtual screening of Na 1.5 inhibitors.

Journal of cellular and molecular medicine
Na 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion chan...

A deep learning platform to assess drug proarrhythmia risk.

Cell stem cell
Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinica...