Neural mass spatio-temporal modeling from high-density electrode array recordings
Neural mass models provide an attractive framework for modeling complex behavior in cortical circuits. The models are based on describing the dynamics of large neural populations through the space and time evolution of a small number of key aggregate statistical quantities. Fitting these models to electrode array recordings can provide insight into connectivity and structure of neural circuits as well as the response of these circuits to stimuli. However, neural mass models are fundamentally nonlinear dynamical systems with large numbers of hidden states, and validating the models on actual recordings and estimating the key parameters remains challenging. This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data. The methodology requires minimal assumptions on the model, and can automatically uncover the underlying components in the neural populations We discuss possible applications to in vivo recordings from feline visual cortex using a recently-developed, high-density 360 contact flexible electrode array with 500 μm inter-electrode spacing.