Automatic discovery of cell types and microcircuitry from neural connectomics

TL;DR

This paper introduces a nonparametric Bayesian method for analyzing neural connectomics data to automatically identify neuron types and microcircuitry, improving understanding of neural structure and function.

Contribution

It presents a novel probabilistic approach that integrates multiple biological data types to discover neural cell types and circuitry patterns automatically.

Findings

Successfully recovers known neuron types in the retina.

Predicts neural connectivity more accurately than simpler algorithms.

Reveals structure in C. elegans nervous system and a microprocessor.

Abstract

Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a nonparametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists, including connectivity, cell body location and the spatial distribution of synapses, in a principled and probabilistically-coherent manner. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of C. elegans, and automatically discovers the structure of a microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.