Neural fields are spatially continuous state variables described by integro-differential equation... more Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.► We derive a multi-resolution estimator of continuum neural field parameters. ► The Macroscopic and microscopic dynamics of the system can be shown simultaneously. ► We show how to infer an arbitrary shaped intracortical connectivity kernel from data.
This paper presents a framework for creating neural field models from electrophysiological data. ... more This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch–Tung–Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.►We derive a model-based, data-driven estimator of continuum neural field parameters. ►Systematic field decomposition enables a finite-dimensional state-space model. ►We show how to infer intracortical connectivity and synaptic dynamics from data. ►Our framework provides a new link between theoretical and experimental neuroscience.
Neural fields are spatially continuous state variables described by integro-differential equation... more Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.► We derive a multi-resolution estimator of continuum neural field parameters. ► The Macroscopic and microscopic dynamics of the system can be shown simultaneously. ► We show how to infer an arbitrary shaped intracortical connectivity kernel from data.
This paper presents a framework for creating neural field models from electrophysiological data. ... more This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch–Tung–Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.►We derive a model-based, data-driven estimator of continuum neural field parameters. ►Systematic field decomposition enables a finite-dimensional state-space model. ►We show how to infer intracortical connectivity and synaptic dynamics from data. ►Our framework provides a new link between theoretical and experimental neuroscience.
Uploads
Papers by Parham Aram