Dynamical Models in Neuroscience From a Closed-Loop Control Perspective

Martínez, Sebastián; García-Violini, Demián; Belluscio, Mariano; Piriz, Joaquín; Peña, Ricardo

doi:10.1109/rbme.2022.3180559

Cited by 8 publications

(7 citation statements)

References 145 publications

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“…Nonlinear differential equations are often used to describe the evolution of neuron populations [1]. The functional interactions between the different agents, e.g.…”

Section: The Wc Modelmentioning

confidence: 99%

“…The algebraic nonlinearity f j of the WC model in equation (1), should be subject to a Jacobian linearisation on different points of the sigmoid curve, according to the current input levels α j in equation (2). The smoothness property of f j assures the existence of the derivative in any point, and thus, it can be parameterised by its slope m j (t) and an offset b j (t) as a linear function 7 .…”

Section: Qlpv Derivation For the Wc Modelmentioning

confidence: 99%

“…Neurobiological models can be categorised into two main groups: single-neuron models and population models [1]. Particularly, population models, also labelled as neural-mass/neural-field models, provide a comprehensive representation of large groups of neurons, enabling the study of collective dynamics and emergent properties [2].…”

Section: Introductionmentioning

confidence: 99%

“…Neural models often exhibit diverse phenomena such as multi-stability behaviours, bifurcation points, and even chaotic dynamics, adding to the analytical complexity [5]. Consequently, approaching these models, particularly from the perspective of automatic control, can be impractical and sometimes prohibitive [1]. However, it is vital to underscore the essential role of automatic control in advancing medical therapies and deepening the understanding of cognitive activities.…”

Section: Introductionmentioning

confidence: 99%

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Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*

Martínez,

Sánchez-Peña,

García-Violini

2024

J. Neural Eng.

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Objective: This paper aims to bridge the gap between neurophysiology and automatic control methodologies by redefining the Wilson-Cowan (WC) model as a control-oriented linear parameter-varying (LPV) system. A novel approach is presented that allows for the application of a control strategy to modulate and track neural activity. Methods: The WC model is redefined as a control-oriented LPV system in this study. The LPV modelling framework is leveraged to design an LPV controller, which is used to regulate and manipulate neural dynamics. Results: Promising outcomes, in understanding and control-ling neural processes through the synergistic combination of control-oriented modelling and estimation, are obtained in this study. An LPV controller demonstrates to be effective in regulating neural activity. Conclusion: The presented methodology effectively induces neural patterns, taking into account optogenetic actuation. The combination of control strategies with neurophysiology provides valuable insights into neural dynamics. Significance: The proposedapproach opens up new possibilities for using control techniques to study and influence brain functions, which can have key implications in neuroscience and medicine. By means of a model-based controller which accounts for non-linearities, noise and uncertainty, neural signals can be induced on brain structures.

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“…Nonlinear differential equations are often used to describe the evolution of neuron populations [1]. The functional interactions between the different agents, e.g.…”

Section: The Wc Modelmentioning

confidence: 99%

Section: Qlpv Derivation For the Wc Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*

Martínez,

Sánchez-Peña,

García-Violini

2024

J. Neural Eng.

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“…Control-oriented models for closed-loop applications in the area of neuroscience provide new opportunities for brain stimulation devices that have ability to affect the brain in order to drive the brain process from a current state to a desired state [13]. A basic block diagram of a closed-loop system for dose titration is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Closing the loop between brain and electrical stimulation: Towards precision neuromodulation treatments

Soleimani¹,

Nitsche²,

Bergmann³

et al. 2022

Preprint

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One of the most critical challenges in using non-invasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. The optimal way to target and apply NIBS in an individual way is yet to be determined while there is a growing body of evidence for its feasibility and effectiveness. Transcranial electrical stimulation (tES) as one of the NIBS techniques has shown promising results in modulating treatment outcomes in several psychiatric and neurologic disorders while faces the same challenge for individual optimization. With the new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to make closed-loop tES-fMRI for individually optimized neuromodulation. The closed-loop tES-fMRI systems can optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

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Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

Liu,

Wei,

et al. 2024

Advances in Neurobiology

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Dynamical Models in Neuroscience From a Closed-Loop Control Perspective

Cited by 8 publications

References 145 publications

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*

Closing the loop between brain and electrical stimulation: Towards precision neuromodulation treatments

Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

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Dynamical Models in Neuroscience From a Closed-Loop Control Perspective

Cited by 8 publications

References 145 publications

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model *

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model *

Closing the loop between brain and electrical stimulation: Towards precision neuromodulation treatments

Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

Contact Info

Product

Resources

About

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*

Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model ^*