Topic: low-rank networks

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

Two perspectives can be distinguished in computational neuroscience towards understanding the link between neuronal activity and behaviour.$^1$ On the one hand, focusing on single-neuron responses highlighted that a few functional properties, such as neuronal tuning$^2$ or selectivity,$^3$ often characterise individual responses. These properties determine each neuron's “position” in an abstract space, which we call the “(functional) similarity space”: for example, each cell's preferred head direction (HD) in the mouse HD system determines its position on an abstract ring.$^4$ It is commonly h