首页|Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks
Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks
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NETL
NSTL
IEEE
This article studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly nonconvex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, nonconvex objective function. We prove that the minimizer of the objective function is unique and establish that the solution of the optimization problem leads to the identification of all the desired system parameters. These results are the basis to introduce an algorithm to find the optimizer by searching the different regions corresponding to the domain of definition of the objective function. To deal with measurement noise in sampled data, we propose a modification of the original algorithm whose identification error is linearly bounded by the magnitude of the measurement noise. We demonstrate the effectiveness of the proposed algorithms through simulations on synthetic and experimental data.
FiringBrain modelingNeuronsBiological system modelingNoiseNoise measurementComputational modelingData modelsAnalytical modelsVectors
Xuan Wang、Jorge Cortés
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Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA, USA
NETL
NSTL
IEEE
