Accelerating input-output model estimation with parallel computing for testing hippocampal memory prostheses in human

Xiwei She; Brian Robinson; Garrett Flynn; Theodore W Berger; Dong Song

doi:10.1016/j.jneumeth.2022.109492

Accelerating input-output model estimation with parallel computing for testing hippocampal memory prostheses in human

J Neurosci Methods. 2022 Mar 15:370:109492. doi: 10.1016/j.jneumeth.2022.109492. Epub 2022 Jan 31.

Authors

Xiwei She¹, Brian Robinson², Garrett Flynn³, Theodore W Berger³, Dong Song⁴

Affiliations

¹ Biomedical Engineering, University of Southern California, Los Angeles, CA, United States. Electronic address: xiweishe@usc.edu.
² The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States.
³ Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.
⁴ Biomedical Engineering, University of Southern California, Los Angeles, CA, United States. Electronic address: dsong@usc.edu.

PMID: 35104492
DOI: 10.1016/j.jneumeth.2022.109492

Abstract

Background: Hippocampal memory prosthesis is defined as a closed-loop biomimetic system that can be used for restoration and enhancement of memory functions impaired in diseases or injuries. To build such a prosthesis, we have developed two types of input-output models, i.e., a multi-input multi-output (MIMO) model for predicting output spike trains based on input spikes, and a double-layer multi-resolution memory decoding (MD) model for classifying spatio-temporal patterns of spikes into memory categories. Both models can achieve high prediction accuracy using human hippocampal spikes data and can be used to derive electrical stimulation patterns to test the hippocampal memory prosthesis.

Methods: However, testing hippocampal memory prostheses in human epilepsy patients with such models has to be performed within a much shorter time window (48-72 h) due to clinical limitations. To solve this problem, we have developed parallelization strategies to decompose the overall model estimation task into multiple independent sub-tasks involving different outputs and cross-validation folds. These sub-tasks are then accomplished in parallel on different computer nodes to reduce model estimation time.

Results: Implementing both parallel schemes with a high-performance computer cluster, we successfully reduced the computing time of model estimations from hundreds of hours to tens of hours.

Comparison with existing method: We have tested the two parallel computing schemes for both MIMO and MD models with data collected from 11 human subjects. The performances of the parallel schemes are compared with the performance of the non-parallel scheme.

Conclusion: Such strategies allow us to complete the modeling procedure within the required time frame to further test input-output model-driven electrical stimulations for the hippocampal memory prosthesis. It has important implications to test the model-based DBS intraoperatively and developing clinically viable hippocampal memory prostheses.

Keywords: Cortical prostheses; High-performance computing cluster; Hippocampus; Machine learning; Memory; Parallel computing.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Artificial Limbs*
Electric Stimulation
Hippocampus / physiology
Humans
Memory* / physiology
Nonlinear Dynamics