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Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed.

Ian D Jordan^1,2, Il Memming Park^1,2,3

¹Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

Researchers developed an analog circuit modeling brain dynamics to test new deep brain stimulation strategies. This work aims to improve treatments for disorders of consciousness and motor control by enabling intelligent attractor transitions.

Keywords:

analog circuit birhythmic bistability dynamical systems neurostimulation

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Area of Science:

Neuroscience
Nonlinear Dynamics
Circuit Design

Background:

Brain dynamics exhibit nonlinear oscillations and multistability, crucial for cognitive functions.
Disorders of consciousness and motor control may stem from an inability to transition between brain states (attractors).
Current deep brain stimulation (DBS) strategies are often insufficient for inducing necessary state transitions.

Purpose of the Study:

To develop a platform for testing next-generation neural stimulators capable of inducing attractor transitions.
To model multistable brain dynamics using an analog circuit.
To investigate state-dependent nonlinear circuit interfaces for effective external perturbation.

Main Methods:

Designed an analog circuit simulating a 3D continuous-time gated recurrent neural network with two stable limit cycles.
Implemented a state-dependent nonlinear circuit interface to manage external perturbations.
Tested the circuit's response to various perturbation strategies to induce transitions between stable oscillations.

Main Results:

The analog circuit successfully modeled multistable brain dynamics with spontaneous oscillations on two periods.
A state-dependent nonlinear interface was effective in discouraging simple perturbation strategies.
Demonstrated the existence of nontrivial solutions for inducing attractor transitions within the circuit model.

Conclusions:

The developed analog circuit serves as a viable platform for testing advanced neural stimulation techniques.
State-dependent nonlinear interfaces are crucial for intelligent attractor transition induction.
This research paves the way for developing adaptive DBS systems for neurological disorders.