Automatic discovery of cell types and microcircuitry from neural connectomics.

Eric Jonas,Konrad Kording

doi:10.7554/elife.04250

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 non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. 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 Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.

Highlights

Emerging connectomics techniques[1,2] promise to quantify the location and connectivity of each neuron within a tissue volume
We perform joint probabilistic inference to automatically learn the number of cell types, which cells belong to which type, their type-specific connectivity, and how connections between types vary with distance
We have presented a machine learning technique that allows cell types and microcircuitry to be discovered from connectomics data

Summary

Introduction

Emerging connectomics techniques[1,2] promise to quantify the location and connectivity of each neuron within a tissue volume These massive datasets will far exceed the capacity of neuroanatomists to manually trace small circuits, necessitating computational, quantitative, and automatic methods for understanding neural circuit structure. The idea of well defined, type-dependent local connectivity patterns (microcircuits) has been prominent in many areas, from sensory (e.g. retina,[4] to processing (e.g. neocortex5) to movement (e.g. spinal cord)[6]. These sorts of repeated computing patterns are a common feature of computing systems, even arising in human-made computing circuits. It remains an important challenge to develop algorithms to use anatomical data, e.g. connectomics, to automatically back out underlying microcircuitry

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Results

Conclusion