AIMC Topic: Action Potentials

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Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks.

Journal of neural engineering Jan 31, 2020
OBJECTIVE: Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, periph...
Electrodes, Implanted Rats, Long-Evans Neural Networks, Computer Animals Action Potentials Peripheral Nerves Rats
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Spike detection and sorting with deep learning.

Journal of neural engineering Jan 24, 2020
OBJECTIVE: The extraction and identification of single-unit activities in intracortically recorded electric signals have a key role in basic neuroscience, but also in applied fields, like in the development of high-accuracy brain-computer interfaces....
Neural Networks, Computer Algorithms Rats Animals Action Potentials Deep Learning Somatosensory Cortex Rats, Wistar
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Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Jan 20, 2020
Although double-precision floating-point arithmetic currently dominates high-performance computing, there is increasing interest in smaller and simpler arithmetic types. The main reasons are potential improvements in energy efficiency and memory foot...
Humans Machine Learning Software Models, Neurological Algorithms Action Potentials Computer Simulation Stochastic Processes
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Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals.

Computers in biology and medicine Dec 2, 2019
"Bad channels" in implantable multi-channel recordings bring troubles into the precise quantitative description and analysis of neural signals, especially in the current "big data" era. In this paper, we combine multimodal features based on local fie...
Brain-Computer Interfaces Machine Learning Action Potentials
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Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network.

Scientific reports Dec 2, 2019
In this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) a...
Signal Processing, Computer-Assisted Electrophysiology Urinary Bladder Rats, Wistar Action Potentials Electrodes, Implanted Posterior Horn Cells Animals Male Neural Networks, Computer
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Modeling of Brain-Like Concept Coding with Adulthood Neurogenesis in the Dentate Gyrus.

Computational intelligence and neuroscience Nov 3, 2019
Mammalian brains respond to new concepts via a type of neural coding termed "concept coding." During concept coding, the dentate gyrus (DG) plays a vital role in pattern separation and pattern integration of concepts because it is a brain region with...
Computer Simulation Neurogenesis Neurons Animals Concept Formation Dentate Gyrus Models, Neurological Humans Association Neuronal Plasticity Action Potentials Neural Networks, Computer
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Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection.

Journal of neural engineering Oct 23, 2019
OBJECTIVE: The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in deep brain stimulation (DBS). To eliminate variability in a neurosurgeon's judgment, this study pres...
Stereotaxic Techniques Subthalamic Nucleus Unsupervised Machine Learning Deep Brain Stimulation Microelectrodes Action Potentials Parkinson Disease Humans
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Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly.

Neural networks : the official journal of the International Neural Network Society Oct 16, 2019
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-s...
Neurons Brain Models, Neurological Action Potentials Neural Networks, Computer Machine Learning
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Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses.

Neural computation Oct 15, 2019
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...
Neurons Synapses Reinforcement, Psychology Neural Networks, Computer Neuronal Plasticity Synaptic Transmission Models, Neurological Computer Simulation Action Potentials
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A review of learning in biologically plausible spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Oct 11, 2019
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by ...
Learning Neuronal Plasticity Action Potentials Neurons Models, Neurological Neural Networks, Computer Algorithms Brain Humans
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