@article {69634, title = {Why do we have so many excitatory neurons?}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 05}, abstract = {

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{\textquoteright}s ability to solve complex problems. Using the larval Drosophila whole-brain electron microscopy connectome, 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.

}, doi = {10.1101/2024.09.24.614724}, url = {https://doi.org/10.1101/2024.09.24.614724}, author = {Wang, Qingyang and Cardona, Albert and Zlatic, Marta and Vogelstein, Joshua T. and Priebe, Carey E.} } @article {48495, title = {Semiparametric spectral modeling of the Drosophila connectome.}, journal = {arXiv}, year = {2017}, month = {2017 May 9}, pages = {1705.03297}, abstract = {

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

}, url = {https://arxiv.org/abs/1705.03297}, author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert} } @article {6911, title = {Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning}, journal = {Science}, volume = {344}, year = {2014}, month = {2014 Mar 27}, pages = {386-92}, chapter = {386}, abstract = {

A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally different approach to neuron-behavior mapping. We optogenetically activated 1,054 identified neuron lines in Drosophila larva and tracked the behavioral responses from 37,780 animals. Applying multiscale unsupervised structure learning methods to the behavioral data identified 29 discrete statistically distinguishable and observer-unbiased behavioral phenotypes. Mapping the neural lines to the behavior(s) they evoke provides a behavioral reference atlas for neuron subsets covering a large fraction of larval neurons. This atlas is a starting point for connectivity- and activity-mapping studies to further investigate the mechanisms by which neurons mediate diverse behaviors.

}, issn = {1095-9203}, doi = {10.1126/science.1250298}, author = {Vogelstein, Joshua T and Park, Youngser and Ohyama, Tomoko and Kerr, Rex and Truman, James W and Priebe, Carey E. and Zlatic, Marta} }