2013/11/29 by Eli Shlizerman, Jeffrey A. Riffell, J. Nathan Kutz
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Neuroscience · #Algorithm #Antennal lobe #Artificial intelligence #Artificial neural network #Biological neural network #Biology #Computer science #Connectome #Excitatory postsynaptic potential #Functional connectivity #Inhibitory postsynaptic potential #Insect Pheromone Research and Control #Insect and Arachnid Ecology and Behavior #Machine learning #Neural coding #Neurobiology and Insect Physiology Research #Neuroscience #Olfactory system #Projection (relational algebra) #Robustness (evolution) #q-bio.NC
paper · pdf · doi:10.3389/fncom.2014.00070
arxiv created 2013/11/29 · openalex publication_date 2014/08/13 · arxiv updated 2014/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.