title: Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.
abstract: The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.
authors.affiliation: Department of Applied Mathematics, University of Washington Seattle, WA, USA.
authors.forename: Eli
authors.initials: E
authors.lastname: Shlizerman
authors.affiliation: Department of Biology, University of Washington Seattle, WA, USA.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Riffell
authors.affiliation: Department of Applied Mathematics, University of Washington Seattle, WA, USA.
authors.forename: J Nathan
authors.initials: JN
authors.lastname: Kutz
keywords: contrast enhancement; data-driven modeling; insect olfaction; neuronal networks; odor discrimination; olfactory neural coding; reduced dynamics