Tatjana Tchumatchenko | Rheinische Friedrich-Wilhelms-Universität Bonn

Dynamics of spiking neural networks in the context of experimental connectivity data 

Abstract: Neural computations emerge from recurrent neural circuits that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. Currently, it is challenging to predict which spiking network connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Additionally, the observation that experimentally reported connectivity measures can vary by orders of magnitude can make it hard to choose biologically plausible connectivity range.  In this talk, I will present two of our recent projects. First, I will present our new results on how synaptic connectivity laws can be identified from variable measurements using a combination of theory and activity recordings. Second, I will discuss the dynamical activity regimes of spiking neural networks with biologically plausible connectivity and activity range. Part of the work has appeared in a preprint https://www.biorxiv.org/content/10.1101/2022.04.22.489005v1