Dorsal Excitor motor neuron DE-3 in the medicinal leech plays three very different dynamical roles in three different behaviors. Without rewiring its anatomical connectivity, how can a motor neuron dynamically switch roles to play appropriate roles in various behaviors? We previously used voltage-sensitive dye imaging to record from DE-3 and most other neurons in the leech segmental ganglion during (fictive) swimming, crawling, and local-bend escape (Tomina and Wagenaar, 2017). Here, we repeated that experiment, then re-imaged the same ganglion using serial blockface electron microscopy and traced DE-3's processes. Further, we traced back the processes of DE-3's presynaptic partners to their respective somata. This allowed us to analyze the relationship between circuit anatomy and the activity patterns it sustains. We found that input synapses important for all of the behaviors were widely distributed over DE-3's branches, yet that functional clusters were different during (fictive) swimming vs. crawling.
The easiest way to access the raw electrophysiology and voltage-dye data as well as the tracing results used in this paper is through a series of Python modules that we made available at https://github.com/wagenadl/leechem-public. Included in the package is a file called "demo.py" that demonstrates the use of the modules. Table 4 lists the available VSD trials.The aligned EM volume may be accessed through the Neuroglancer instance at https://leechem.caltech.edu or by pointing SBEMViewer to https://leechem.caltech.edu/emdata.The code used for alignment is available at https://github.com/wagenadl/sbemalign. Our visualization tools SBEMViewer and GVox are at https://github.com/wagenadl/sbemviewer and https://github.com/wagenadl/gvox.
Code and data access forGithub, wagenadl/leechem.
- William B Kristan Jnr
- Mark H Ellisman
- Daniel A Wagenaar
- Mark H Ellisman
- Yusuke Tomina
- Yusuke Tomina
- Pegah Kassraian
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Vatsala Thirumalai, National Centre for Biological Sciences, India
© 2021, Ashaber et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
All animals face the challenge of finding nutritious resources in a changing environment. To maximize life-time fitness, the exploratory behavior has to be flexible, but which behavioral elements adapt and what triggers those changes remain elusive. Using experiments and modeling, we characterized extensively how Drosophila larvae foraging adapts to different food quality and distribution and how the foraging genetic background influences this adaptation. Our work shows that different food properties modulated specific motor programs. Food quality controls the travelled distance by modulating crawling speed and frequency of pauses and turns. Food distribution, and in particular the food-no food interphase, controls turning behavior, stimulating turns towards the food when reaching the patch border and increasing the proportion of time spent within patches of food. Finally, the polymorphism in the foraging gene (rover-sitter) of the larvae adjusts the magnitude of the behavioral response to different food conditions. This study defines several levels of control of foraging and provides the basis for the systematic identification of the neuronal circuits and mechanisms controlling each behavioral response.
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.