Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.
|Original language||American English|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - 17 Jan 2023|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. We thank the anonymous reviewers who provided very helpful comments. The Zaslaver group was supported by ERC (336803), ISF (1300/17), and ICORE (1902/12). R.R was also supported by the Jerusalem Brain Center. S.W.E. was supported by NIMH grant R01MH112689. S.W.E. is the Siegfried Ullmann chair in Genetics.A.Z.is the Greenfield chair in Neurobiology.
Copyright © 2023 the Author(s). Published by PNAS.
- C. elegans
- neurite computation