Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons

Gayathri Vivekanandhan; Mahtab Mehrabbeik; Karthikeyan Rajagopal; Sajad Jafari; Stephen G. Lomber; Yaser Merrikhi; Gayathri Vivekanandhan; Mahtab Mehrabbeik; Karthikeyan Rajagopal; Sajad Jafari; Stephen G. Lomber; Yaser Merrikhi

doi:10.3934/mbe.2023151

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 3216-3236. doi: 10.3934/mbe.2023151

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Research article

Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons

1.
Centre for Artificial Intelligence, Chennai Institute of Technology, India
2.
Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
3.
Centre for Nonlinear Systems, Chennai Institute of Technology, India
4.
Department of Electronics and Communications Engineering and University Centre of Research & Development, Chandigarh University, Mohali 140413, Punjab
5.
Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran
6.
Department of Physiology, Faculty of Medicine, McGill University, Montreal, H3G 1Y6, Canada

Received: 13 August 2022 Revised: 18 November 2022 Accepted: 21 November 2022 Published: 02 December 2022

Neural signatures of working memory have been frequently identified in the spiking activity of different brain areas. However, some studies reported no memory-related change in the spiking activity of the middle temporal (MT) area in the visual cortex. However, recently it was shown that the content of working memory is reflected as an increase in the dimensionality of the average spiking activity of the MT neurons. This study aimed to find the features that can reveal memory-related changes with the help of machine-learning algorithms. In this regard, different linear and nonlinear features were obtained from the neuronal spiking activity during the presence and absence of working memory. To select the optimum features, the Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization methods were employed. The classification was performed using the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers. Our results suggest that the deployment of spatial working memory can be perfectly detected from spiking patterns of MT neurons with an accuracy of 99.65±0.12 using the KNN and 99.50±0.26 using the SVM classifiers.
- working memory,
- middle temporal area,
- firing rate signal,
- machine-learning algorithm,
- classification
Citation: Gayathri Vivekanandhan, Mahtab Mehrabbeik, Karthikeyan Rajagopal, Sajad Jafari, Stephen G. Lomber, Yaser Merrikhi. Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 3216-3236. doi: 10.3934/mbe.2023151

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Abstract

Neural signatures of working memory have been frequently identified in the spiking activity of different brain areas. However, some studies reported no memory-related change in the spiking activity of the middle temporal (MT) area in the visual cortex. However, recently it was shown that the content of working memory is reflected as an increase in the dimensionality of the average spiking activity of the MT neurons. This study aimed to find the features that can reveal memory-related changes with the help of machine-learning algorithms. In this regard, different linear and nonlinear features were obtained from the neuronal spiking activity during the presence and absence of working memory. To select the optimum features, the Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization methods were employed. The classification was performed using the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers. Our results suggest that the deployment of spatial working memory can be perfectly detected from spiking patterns of MT neurons with an accuracy of 99.65±0.12 using the KNN and 99.50±0.26 using the SVM classifiers.

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