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

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) m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of neuroscience methods Jg. 370; S. 109492
Hauptverfasser: She, Xiwei, Robinson, Brian, Flynn, Garrett, Berger, Theodore W., Song, Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 15.03.2022
Schlagworte:
Artificial Limbs
Cortical prostheses
Electric Stimulation
High-performance computing cluster
Hippocampus
Hippocampus - physiology
Humans
Machine learning
Memory
Memory - physiology
Nonlinear Dynamics
Parallel computing
Parallel computing
High-performance computing cluster
Memory
Hippocampus
Machine learning
Cortical prostheses
ISSN:0165-0270, 1872-678X, 1872-678X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. 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. 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. 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. 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. •We propose parallelization schemes for models to reduce their computing time.•We have tested the parallelization strategy with data from 11 human subjects.•The proposed schemes are significantly faster than the non-parallel scheme.•We provide an open-source toolbox for users to develop their own parallel schemes.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0165-0270
1872-678X
1872-678X
DOI:10.1016/j.jneumeth.2022.109492