Paper Info
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.
Year
DOI
Venue
2014
10.3389/fncom.2014.00070
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
data-driven modeling,reduced dynamics,olfactory neural coding,neuronal networks,odor discrimination,contrast enhancement,insect olfaction
Neuroscience,Computer science,Connectome,Robustness (computer science),Inhibitory postsynaptic potential,Artificial intelligence,Stimulus (physiology),Spiking neural network,Biological neural network,Antennal lobe,Electrophysiology,Machine learning
Journal
Volume
ISSN
Citations 
8
1662-5188
5
PageRank 
References 
Authors
0.52
3
3
Name
Order
Citations
PageRank
Eli Shlizerman1184.73
Jeffrey A Riffell271.62
J. Nathan Kutz322547.13