Topic: connectivity structure

The connectivity structure of many biological systems, including neural circuits, is highly non-uniform. Recent technologies allow detailed mapping of these irregularities, but our understanding of their effect on the overall circuit dynamics is still lacking. By developing complex system analytical tools that perform reduction of the network, I determine the impact of connectivity features on network dynamics. I will demonstrate the use of these tools on neural assemblies (clusters), a ubiquitous non-uniform structure in our brains. I will show how neural assemblies of different sizes natural

Recurrent neural networks (RNNs) are proving increasingly useful in systems neuroscience. Training RNNs on cognitive-like tasks has demonstrated their potential to infer dynamical processes and computations in the brain [1, 2]. They have additionally been shown to provide an interpretable link between connectivity structure and activity dynamics in low-rank networks [3]. However, a potentially serious shortcoming is that many (if not most) of these networks disobey biological constraints in terms of their connectivity structure and response properties, which raises questions as to their interp