Graph analysis of looming-selective networks in the tectum, and its replication in a simple computational model

Abstract

AbstractLooming stimuli evoke behavioral responses in most animals, yet the mechanisms of looming detection in vertebrates are poorly understood. Here we hypothesize that looming detection in the tectum may rely on spontaneous emergence of synfire chains: groups of neurons connected to each other in the same sequence in which they are activated during a loom. We then test some specific consequences of this hypothesis. First, we use high-speed calcium imaging to reconstruct functional connectivity of small networks within the tectum of Xenopus tadpoles. We report that reconstructed directed graphs are clustered and hierarchical, that their modularity increases in development, and that looming-selective cells tend to collect activation within these graphs. Second, we describe spontaneous emergence of looming selectivity in a computational developmental model of the tectum, governed by both synaptic and intrinsic plasticity, and driven by structured visual inputs. We show that synfire chains contribute to looming detection in the model; that structured inputs are critical for the emergence of selectivity, and that biological tectal networks follow most, but not all predictions of the model. Finally, we propose a conceptual scheme for understanding the emergence and fine-tuning of collision detection in developing aquatic animals.