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Title: Inferring Network Structure from Neural Activity in a Biologically-Constrained Model of the Insect Olfactory System
Understanding olfactory processing in insects requires characterizing the complex dynamics and connectivity of the first olfactory relay - antennal lobe (AL). We leverage in vivo electrophysiology to train recurrent neural network (RNN) model of the locust AL, inferring the underlying connectivity and temporal dynamics. The RNN comprises 830 projection neurons (PNs) and 300 local neurons (LNs), replicating the locust AL anatomy. The trained network reveals sparse connectivity, with different connection densities between LNs and PNs and no PN-PN connections, consistent with in vivo data. The learned time constants predict slower LN dynamics and diverse PN response patterns, with low and high time constants correlating with early and late odor-evoked activity, as reported in vivo. Our approach demonstrates the utility of biologically-constrained RNNs in inferring circuit properties from empirical data, providing insights into mechanisms of odor coding in the AL.  more » « less
Award ID(s):
2323241
PAR ID:
10627550
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
https://2024.ccneuro.org/
Date Published:
Format(s):
Medium: X
Location:
Boston, MA
Sponsoring Org:
National Science Foundation
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