Kavli Affiliate: Carey Priebe and Joshua Vogelstein
| Authors: Qingyang Wang, Albert Cardona, Marta Zlatic, Joshua Vogelstein and Carey Priebe
| Summary:
Approximately four in five neurons are excitatory. This is true across functional regions and species. Why do we have so many excitatory neurons? Little is known. Here we provide a normative answer to this question. We designed a task-agnostic, learning-independent and experiment-testable measurement of functional complexity, which quantifies the network’s ability to solve complex problems. Using the first neuron level full connectome of a species—the larva Drosophila—we discovered the optimal Excitatory-Inhibitory (E-I) ratio that maximizes the functional complexity: 75-81% percentage of neurons are excitatory. This number is consistent with the true distribution observed via scRNA-seq. We found that the abundance of excitatory neurons confers an advantage in functional complexity, but only when inhibitory neurons are highly connected. In contrast, when the E-I identities are sampled uniformly (not dependent on connectivity), the optimal E-I ratio falls around equal population size, and its overall achieved functional complexity is sub-optimal. Our functional complexity measurement offers a normative explanation for the over-abundance of excitatory neurons in the brain. We anticipate that this approach will further uncover the functional significance of various neural network structures.