Search

The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists.

Presentation on Hierarchical Optimal Sampling: a tool for managing and manipulating wide-field imaging datasets. Powerful recording techniques and increasing anatomical knowledge provided by atlases allow studying the brain at a new level with unprecedented spatial resolution as that achieved with wide-field calcium imaging data. One of the the main issues related to the increase of the spatial resolution is to find the optimal condition which allows to obtain an appropriate signal-to-noise ratio for each channel, relative to the phenomenon of interest. Hierarchical Optimal Sampling (HOS) provides a data-driven inhomogeneous gridding of the field of view with the optimal spatial resolution that preserves and emphasizes the majority of the information from the high number of native signal sources.

Flash talk presented at "School of Brain Cells & Circuits Camillo Golgi"