Topic: electrophysiological data

Profound changes in the visual environment occur over the course of the day-night cycle. There is therefore a profound pressure for cells and circuits within the visual system to adjust their function over time, to match the prevailing visual environment. Here, I will discuss electrophysiological data collected from nocturnal and diurnal rodents that reveal how the visual code is ‘temporally optimised’ by 1) the retina’s circadian clock, and 2) a change in behavioural temporal niche.

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these da