Neural Circuits and Algorithms

A Brief Introduction to Connetomics

Connectomes are brain wiring diagrams at the synapse level. They are crucial for reverse engineering circuit function but are extremely scarce. Connectome reconstruction is challenging because it requires imaging and identifying axons and dendrites over tens of microns at nanometer resolution.

Currently, the only imaging technology that can bridge this range of scales is serial electron microscopy. The bottleneck in connectome reconstruction lies in the tracing of axons and dendrites, through millions of serial electron micrographs.

Our Connectomics project focuses on Megaphragma viggiani, a highly miniaturised wasp with complex behavior yet a small number of neurons.

Videos

Overview of the Megaphragma Dataset, from Alexy Polilov and the lab of Harald Hess
Flying Megaphragma from four angles, timelapse, from the lab of Alexy Polilov
Flying Megaphragma, timelapse, from the lab of Alexy Polilov
Reconstructed Optic Lobe Neurons in the Megaphragma
1865 of the neurons in the Megaphragma Brain
Megaphragma head FIB-SEM volume, from the lab of Harald Hess

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil—the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the “barebones” circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.

In the field of Connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved remarkable accuracy, errors still exist, especially in regions with image defects. One common type of defect is that of consecutive missing image sections. Here, data is lost along some axis, and the resulting neuron segmentations are split across the gap. To address this problem, we propose a novel method based on point cloud representations of neurons. We formulate the problem as a classification problem and train CurveNet, a state-of-the-art point cloud classification model, to identify which neurons should be merged. We show that our method not only performs strongly but also scales reasonably to gaps well beyond what other methods have attempted to address. Additionally, our point cloud representations are highly efficient in terms of data, maintaining high performance with an amount of data that would be unfeasible for other methods. We believe that this is an indicator of the viability of using point cloud representations for other proofreading tasks.

As the study of the human brain is complicated by its sheer scale, complexity, and impracticality of invasive experiments, neuroscience research has long relied on model organisms. The brains of macaque, mouse, zebrafish, fruit fly, nematode, and others have yielded many secrets that advanced our understanding of the human brain. Here, we propose that adding miniature insects to this collection would reduce the costs and accelerate brain research. The smallest insects occupy a special place among miniature animals: despite their body sizes, comparable to unicellular organisms, they retain complex brains that include thousands of neurons. Their brains possess the advantages of those in insects, such as neuronal identifiability and the connectome stereotypy, yet are smaller and hence easier to map and understand. Finally, the brains of miniature insects offer insights into the evolution of brain design.

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