Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

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Summary

Effective neural stimulation requires adequate parametrization. Gaussian-process (GP)-based Bayesian optimization (BO) offers a framework to discover optimal stimulation parameters in real time. Here, we first provide a general protocol to deploy this framework in neurostimulation interventions and follow by exemplifying its use in detail. Specifically, we describe the steps to implant rats with multi-channel electrode arrays in the hindlimb motor cortex. We then detail how to utilize the GP-BO algorithm to maximize evoked target movements, measured as electromyographic responses.

For complete details on the use and execution of this protocol, please refer to Bonizzato and colleagues (2023). ¹

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Highlights

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A primer on Gaussian-process-based Bayesian optimization algorithm is presented
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General steps for online optimization of neurostimulation interventions are described
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An example intervention where an array and EMG wires are implanted in rats is provided
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Steps for online optimization of this specific intervention in a rat are described

Abstract

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

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Most cited references 18

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Practical Bayesian Optimization of Machine Learning Algorithms

Jasper Snoek, Hugo Larochelle, Ryan P. Adams (2012)

Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of developing automatic approaches which can optimize the performance of a given learning algorithm to the task at hand. In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). The tractable posterior distribution induced by the GP leads to efficient use of the information gathered by previous experiments, enabling optimal choices about what parameters to try next. Here we show how the effects of the Gaussian process prior and the associated inference procedure can have a large impact on the success or failure of Bayesian optimization. We show that thoughtful choices can lead to results that exceed expert-level performance in tuning machine learning algorithms. We also describe new algorithms that take into account the variable cost (duration) of learning experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of contemporary algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.

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Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys.

RJ Nudo, GW Milliken, WM Jenkins … (1996)

This study was undertaken to document plastic changes in the functional topography of primary motor cortex (M1) that are generated in motor skill learning in the normal, intact primate. Intracortical microstimulation mapping techniques were used to derive detailed maps of the representation of movements in the distal forelimb zone of M1 of squirrel monkeys, before and after behavioral training on two different tasks that differentially encouraged specific sets of forelimb movements. After training on a small-object retrieval task, which required skilled use of the digits, their evoked-movement digit representations expanded, whereas their evoked-movement wrist/forearm representational zones contracted. These changes were progressive and reversible. In a second motor skill exercise, a monkey pronated and supinated the forearm in a key (eyebolt)-turning task. In this case, the representation of the forearm expanded, whereas the digit representational zones contracted. These results show that M1 is alterable by use throughout the life of an animal. These studies also revealed that after digit training there was an areal expansion of dual-response representations, that is, cortical sectors over which stimulation produced movements about two or more joints. Movement combinations that were used more frequently after training were selectively magnified in their cortical representations. This close correspondence between changes in behavioral performance and electrophysiologically defined motor representations indicates that a neurophysiological correlate of a motor skill resides in M1 for at least several days after acquisition. The finding that cocontracting muscles in the behavior come to be represented together in the cortex argues that, as in sensory cortices, temporal correlations drive emergent changes in distributed motor cortex representations.

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How somatotopic is the motor cortex hand area?

Marc Schieber, W L Hibbard (1993)

The primary motor cortex (M1) is thought to control movements of different body parts from somatotopically organized cortical territories. Electrical stimulation suggests, however, that territories controlling different fingers overlap. Such overlap might be artifactual or else might indicate that activation of M1 to produce a finger movement occurs over a more widespread cortical area than usually assumed. These possibilities were distinguished in monkeys moving different fingers. Recordings showed that single M1 neurons were active with movements of different fingers. Neuronal populations active with movements of different fingers overlapped extensively. Control of any finger movement thus appears to utilize a population of neurons distributed throughout the M1 hand area rather than a somatotopically segregated population.

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Author and article information

Contributors

Léo Choinière

Numa Dancause

Journal

Journal ID (nlm-ta): STAR Protoc

Journal ID (iso-abbrev): STAR Protoc

Title: STAR Protocols

Publisher: Elsevier

ISSN (Electronic): 2666-1667

Publication date PMC-release: 14 February 2024

Publication date Collection: 15 March 2024

Publication date (Electronic): 14 February 2024

Volume: 5

Issue: 1

Electronic Location Identifier: 102885

Affiliations

[1 ]Department of Neurosciences and Centre Interdisciplinaire de Recherche sur le Cerveau et l’Apprentissage (CIRCA), Université de Montréal, Montreal, QC H3T 1J4, Canada

[2 ]Department of Electrical Engineering and Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada

[3 ]Mila - Québec AI Institute, Montreal, QC H2S 3H1, Canada

[4 ]Mathematics and Statistics Department, Université de Montréal, Montreal, QC H3T 1J4, Canada

Author notes

[∗ ]Corresponding author leo.choiniere@ 123456umontreal.ca

[∗∗ ]Corresponding author numa.dancause@ 123456umontreal.ca

[5]

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[6]

Lead contact

Article

Publisher Item ID: S2666-1667(24)00050-9 Publisher ID: 102885

DOI: 10.1016/j.xpro.2024.102885

PMC ID: 10876592

PubMed ID: 38358881

SO-VID: 21dd006d-253a-4801-915b-bf95a4a297f0

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

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Network Medicine

Most cited references 18

Practical Bayesian Optimization of Machine Learning Algorithms

Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys.

How somatotopic is the motor cortex hand area?

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