Neural information flow: Learning neural information processing systems from brain activity

Abstract

Neural information flow (NIF) is a new framework for system identification in neuroscience. NIF models represent neural information processing systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region via linear observation models. NIF models are trained via backpropagation, directly leveraging the neural signal as the loss. Trained NIF models are accessible for in silico analyses. Using a large-scale fMRI video stimulation dataset and a feed-forward convolutional neural network-based NIF model as an example we show that, in this manner, we can estimate models that learn meaningful neural computations and representations. Our framework is general in the sense that it can be used in conjunction with any neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of high-dimensional neural datasets.