“A Connectome of the Drosophila Central Complex Reveals Network Motifs Suitable for Flexible Navigation and Context-Dependent Action Selection”, 2021-10-26 (; backlinks; similar):
[media] Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context/experience-dependent spatial navigation.
We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution.
We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly’s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.
…Here we analyzed the arborizations and connectivity of the ~3,000 CX neurons in version 1.1 of the ‘hemibrain’ connectome—a dataset with 25,000 semi-automatically reconstructed neurons and 20 million synapses from the central brain of a 5-day-old female fly ( et al 2020) (see Method).
…EM circuit reconstruction: how complete is complete enough? The value of EM-level connectomes in understanding the function of neural circuits in small and large brains is widely appreciated ( et al 2020; Litwin-2019; et al 2017). Although recent technical advances have made it possible to acquire larger EM volumes ( et al 2020; et al 2018) and improvements in machine learning have enabled high-throughput reconstruction of larger neural circuits ( et al 2020; et al 2018), the step from acquiring a volume to obtaining a complete connectome still requires considerable human proofreading and tracing effort ( et al 2020).
As part of our analysis of the CX connectome, we found that although increased proofreading led to an expected increase in the number of synaptic connections between neurons, it did not necessarily lead to substantial changes in the relative weight of connections between different neuron types (Figures 3–4). While it is important to note that we made comparisons between the hemibrain connectome at fairly advanced stages of proofreading in the CX, our results do suggest that it may be possible to obtain an accurate picture of neural circuit connectivity from incomplete reconstructions. It may be useful for future large scale connectomics efforts to incorporate similar validation steps of smaller sample volumes into reconstruction pipelines to determine appropriate trade-offs between accuracy and cost of proofreading.
See Also:
“A visual motion detection circuit suggested by Drosophila connectomics”
“Dense connectomic reconstruction in layer 4 of the somatosensory cortex”
“Deep learning models of cognitive processes constrained by human brain connectomes”
“A massive 7T fMRI dataset to bridge cognitive and computational neuroscience”
“Connectomic reconstruction of the inner plexiform layer in the mouse retina”
“High-throughput mapping of a whole rhesus monkey brain at micrometer resolution”