ArticleFrontiers in computational neuroscience2014
Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Unsupervised learning of control signals and their encodings inJournal of the Royal Society, Interface · 2020Article
- Short term depression, presynaptic inhibition and local neuron diversity play key functional roles in the insect antennal lobe.Journal of computational neuroscience · 2020Article
- Neural Circuit Dynamics for Sensory Detection.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Neural Interactome: Interactive Simulation of a Neuronal System.Frontiers in computational neuroscience · 2019Article
- Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in theFrontiers in computational neuroscience · 2018Article
- Symmetries Constrain Dynamics in a Family of Balanced Neural Networks.Journal of mathematical neuroscience · 2017Article
- Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases.Journal of computational neuroscience · 2017Article
- Classification of Fixed Point Network Dynamics from Multiple Node Timeseries Data.Frontiers in neuroinformatics · 2017Article
- Multistability and Long-Timescale Transients Encoded by Network Structure in a Model ofFrontiers in computational neuroscience · 2017Article
- Dimensionality reduction in neuroscience.Current biology : CB · 2016Article
- Nonlinear control of networked dynamical systems.IEEE transactions on network science and engineeringArticle
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Authors and funding
3 authors.
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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.
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