Abstract: Early brain activity looks noisy, but it obeys consistent statistical and dynamical laws. We argue that these laws arise from a preconfigured circuit architecture that forms before sensory experience and constrains later computation and behavior. We show this across scales. At the synaptic and single-unit level, large-scale electrophysiology across development reveals early, persistent signatures of organization: heavy-tailed firing-rate distributions, stable interaction statistics, and a hub-like, “oligarchical” structure. Computational modeling shows that these features cohere as intrinsic constraints rather than products of learning. This framework extends to brain organoids, a model that is, by definition, devoid of sensory experience. In human and mouse organoids, structured firing sequences and circuit topology emerge spontaneously, giving a controlled system to test what network organization development can generate on its own, and what truly requires external input. If circuit structure precedes experience, it should support computation from birth. We therefore turn to behavior and ask whether higher-order cortex can influence neonatal actions, contrary to the common view that it matures too slowly to matter. Focusing on survival-relevant innate behaviors, we combine in vivo electrophysiology, opto- and chemogenetics, anatomical tracing, and modeling to show that neonatal prefrontal circuits exert causal top-down control over light avoidance and ultrasonic vocalizations through defined cortical and cortico-striatal pathways. Together, these results support a synapse-to-network-to-behavior account: early architecture sets the scaffold, firing sequences tunes the operating regime, and higher-order circuits shape behavior well before classical sensory learning.
Bio: Mattia Chini is a physician-scientist and newly appointed PI at GIGA Neurosciences (Université de Liège), with a joint affiliation at University Medical Center Hamburg-Eppendorf. His lab studies how inhibitory circuits mature during early development and how this maturation reshapes network phenomena, from sparsification and spiking decorrelation to the emergence of structured rhythms. Using large-scale in vivo electrophysiology, cell-type-resolved circuit interrogation, and quantitative modeling, he has shown that key features of brain activity, ranging from synapses to innate behaviors, are already present early in life, supporting the preconfigured brain hypothesis. He uses these quantitative signatures to reverse-engineer the circuit mechanisms that organize developing brain dynamics.
Invited by: Athena Demertzi