Structural basIs of Neural Computation and Behavior – SiNCoBe

Behavior and decision-making are determined by physical processes taking place in the complex environment explored by the animal. During the last 3-5 years, breakthroughs in experimental techniques have made it possible to map the wiring diagram (the biological neural connectome) of the full central nervous systems of simple model organisms at the level of single synapses, as well as portions of the brain for higher animals. Moreover, we can now control the activity of individual neurons in freely behaving animals through optogenetic stimulation and record the resulting behavior. Together this offers the occasion to reverse engineer the physical basis of behavior.

SiNCoBe aims to leverage simultaneous access to the connectome of the Drosophila melanogaster larva and to existing data from large-scale behavior screens, which record the effect of individual activation or silencing of the majority of the larva’s neurons, in order to address the following question: What constraints do the structure of the connectome impose on an organism’s capability to process information and encode behavior? Specifically, it will combine modern methods from machine learning and network science to investigate how the circuitry of the Drosophila larva’s nervous system influences the way it encodes behavior.

This project involves three complementary sub-objectives:

O1. To understand how the structure of neural circuits influences the way they encode behavior by training artificial neural networks (ANNs) to solve the same behavioral tasks that empirical circuits have been experimentally demonstrated to do.

O2. To provide a robust statistical characterization of the structure of the larva’s central nervous system, by quantifying the global and local abundances of neural microcircuits and test the hypothesis that neural computations are performed by a restricted number of canonical microcircuits.

O3. To provide experimentally testable predictions of functional microcircuits in the larva’s central nervous system by combining the results and methods of O1 and O2.