title: Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations.
abstract: The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.
authors.affiliation: Bernstein Center for Computational Neuroscience, Berlin, Germany.|Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.|Group for Methods of Artificial Intelligence, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
authors.forename: Christian
authors.identifier: 0000-0002-4499-2895
authors.initials: C
authors.lastname: Donner
authors.affiliation: Bernstein Center for Computational Neuroscience, Berlin, Germany.|Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Obermayer
authors.affiliation: RIKEN Brain Science Institute, Wako-shi, Saitama, Japan.
authors.forename: Hideaki
authors.identifier: 0000-0001-7794-3064
authors.initials: H
authors.lastname: Shimazaki
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors