AIMC Topic: Action Potentials

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Effective and efficient neural networks for spike inference from calcium imaging.

Cell reports methods
Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a...

A deep learning network based on CNN and sliding window LSTM for spike sorting.

Computers in biology and medicine
Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need...

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...