Robust Transcoding Sensory Information With Neural Spikes

Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies only aim at dealing with the continuous signal of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 33; no. 5; pp. 1935 - 1946
Main Authors: Xu, Qi, Shen, Jiangrong, Ran, Xuming, Tang, Huajin, Pan, Gang, Liu, Jian K.
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Action Potentials - physiology
Background noise
Biological information theory
Brain
Brain - physiology
Brain mapping
Brain-Computer Interfaces
Computational modeling
Cross-multimodal
Decoding
denoising
Firing pattern
Functional magnetic resonance imaging
Image reconstruction
Information processing
Magnetic resonance imaging
Man-machine interfaces
Models, Neurological
Nervous system
Neural coding
Neural Networks, Computer
neural spikes
Neuroimaging
Neurons
Neurons - physiology
Reconstruction
Sensory integration
Sensory perception
spatio-temporal representations
Transcoding
Visual stimuli
Visualization
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies only aim at dealing with the continuous signal of neural systems, while lacking a unique feature of biological neurons, termed spike, which is the fundamental information unit for neural computation as well as a building block for brain-machine interface. Aiming at these limitations, we propose a transcoding framework to encode multi-modal sensory information into neural spikes and then reconstruct stimuli from spikes. Sensory information can be compressed into 10% in terms of neural spikes, yet re-extract 100% of information by reconstruction. Our framework can not only feasibly and accurately reconstruct dynamical visual and auditory scenes, but also rebuild the stimulus patterns from functional magnetic resonance imaging (fMRI) brain activities. More importantly, it has a superb ability of noise immunity for various types of artificial noises and background signals. The proposed framework provides efficient ways to perform multimodal feature representation and reconstruction in a high-throughput fashion, with potential usage for efficient neuromorphic computing in a noisy environment.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3107449