Version 1
: Received: 1 August 2023 / Approved: 2 August 2023 / Online: 3 August 2023 (10:46:24 CEST)
How to cite:
Kasabov, N. K.; Bahrami, H.; Doborjeh, M.; Wang, A. Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI. Preprints2023, 2023080333. https://doi.org/10.20944/preprints202308.0333.v1
Kasabov, N. K.; Bahrami, H.; Doborjeh, M.; Wang, A. Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI. Preprints 2023, 2023080333. https://doi.org/10.20944/preprints202308.0333.v1
Kasabov, N. K.; Bahrami, H.; Doborjeh, M.; Wang, A. Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI. Preprints2023, 2023080333. https://doi.org/10.20944/preprints202308.0333.v1
APA Style
Kasabov, N. K., Bahrami, H., Doborjeh, M., & Wang, A. (2023). Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI. Preprints. https://doi.org/10.20944/preprints202308.0333.v1
Chicago/Turabian Style
Kasabov, N. K., Maryam Doborjeh and Alan Wang. 2023 "Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI" Preprints. https://doi.org/10.20944/preprints202308.0333.v1
Abstract
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the infor-mation, either as a limited number of variables, or limited time to make the decision, or both. The brain functions as spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier, that utilizes the NeuCube brain-inspired spiking neural network framework. Here we apply the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. The paper shows that once a NeuCube STAM classification model is trained on a complete spatio-temporal EEG or fMRI data, it can be recalled using only part of the time series, or/and only part of the used variables. We evaluate accordingly the temporal association accuracy and spatial association accuracy. This is a pilot study that opens the field for the development of multimodal classification systems on other multimodal neuroimaging data, such as the also shown longitudinal MRI data, trained on complete data, but recalled on partial data collected across different settings, in different labs and clinics, that may vary in terms of variables, time of data collection, and other parameters. The proposed methods will allow also for brain diagnostic/prognostic marker discovery using spatio-temporal neuroimaging data.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
