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...

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Podrobná bibliografia
Vydané v:	IEEE transaction on neural networks and learning systems Ročník 33; číslo 5; s. 1935 - 1946
Hlavní autori:	Xu, Qi, Shen, Jiangrong, Ran, Xuming, Tang, Huajin, Pan, Gang, Liu, Jian K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.05.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	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