AI Medical Compendium Topic:
Models, Neurological

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An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting.

International journal of neural systems
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training ...

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.

Journal of neural engineering
OBJECTIVE: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and...

Deep learning in spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagat...

A hypothetical neural network model for generation of human precision grip.

Neural networks : the official journal of the International Neural Network Society
Humans can stably hold and skillfully manipulate an object by coordinated control of a complex, redundant musculoskeletal system. However, how the human central nervous system actually accomplishes precision grip tasks by coordinated control of finge...

Asynchronous Multiplex Communication Channels in 2-D Neural Network With Fluctuating Characteristics.

IEEE transactions on neural networks and learning systems
Neurons behave like transistors, but have fluctuating characteristics. In this paper, we show that several asynchronous multiplex communication channels can be established in a 2-D mesh neural network with randomly generated weights between eight nei...

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Human brain mapping
Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regress...

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease ...

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities.

Nature communications
The ability for artificially reproducing human brain type signals' processing is one of the main challenges in modern information technology, being one of the milestones for developing global communicating networks and artificial intelligence. Electr...

Prediction error connectivity: A new method for EEG state analysis.

NeuroImage
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions,...

Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions ...