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Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.

Kaushalya Kumarasinghe¹, Nikola Kasabov², Denise Taylor³

¹Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand; Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.

Neural Networks : the Official Journal of the International Neural Network Society

|October 1, 2019

Summary

Brain-Inspired Spiking Neural Networks (BI-SNNs) reveal deep brain patterns for advanced Brain-Computer Interfaces (BCIs). This framework extracts neural trajectories and rules, enhancing BCI capabilities for understanding brain organization.

Keywords:

Brain-Computer Interface Deep learning Electroencephalography Knowledge representation NeuCube Spiking Neural Networks

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Area of Science:

Neuroscience
Artificial Intelligence
Computational Neuroscience

Background:

Spiking Neural Networks (SNNs) offer a biologically plausible model for neural computation.
Extracting deep spatio-temporal patterns from complex brain data remains a challenge.
Current Brain-Computer Interfaces (BCIs) can be enhanced by deeper understanding of brain function.

Purpose of the Study:

To present a theoretical framework for Brain-Inspired Spiking Neural Network (BI-SNN) architectures.
To demonstrate the capability of BI-SNNs to learn and represent deep spatio-temporal patterns from brain data.
To introduce Brain-Inspired Brain-Computer Interfaces (BI-BCIs) for enhanced BCI applications.

Main Methods:

Development of a computational framework utilizing BI-SNNs for knowledge extraction.
Experimental validation using spatio-temporal data from a Grasp and Lift task.
Representation of extracted patterns as deep spatio-temporal rules.

Main Results:

Successfully extracted neural trajectories representing visual processing streams (dorsal and ventral) and their motor cortex connections.
Identified deep spatio-temporal rules governing functional and structural interactions within brain areas.
Demonstrated the utility of these rules for event prediction in a BI-BCI context.

Conclusions:

The developed computational framework effectively unveils topological brain patterns.
Extracted brain knowledge can significantly enhance the performance and capabilities of state-of-the-art BCIs.
BI-SNNs provide a powerful approach for understanding and interfacing with the brain.