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Research Articles, Systems/Circuits

Neural Circuit Dynamics for Sensory Detection

Sruti Mallik, Srinath Nizampatnam, Anirban Nandi, Debajit Saha, Baranidharan Raman and ShiNung Ching
Journal of Neuroscience 22 April 2020, 40 (17) 3408-3423; DOI: https://doi.org/10.1523/JNEUROSCI.2185-19.2020
Sruti Mallik
1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130
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Srinath Nizampatnam
1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130
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Anirban Nandi
2Allen Institute of Brain Science, Seattle, Washington 98109
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Debajit Saha
3Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan 48824
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Baranidharan Raman
4Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130
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ShiNung Ching
1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130
4Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130
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Abstract

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observed in vivo in locusts (Schistocerca americana) of either sex. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory–inhibitory competitive dynamics, similar to interactions between projection neurons and local neurons in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and trade-offs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection.

SIGNIFICANCE STATEMENT A key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we examine not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. Through optimization-based synthesis, we examine how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.

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The Journal of Neuroscience: 40 (17)
Journal of Neuroscience
Vol. 40, Issue 17
22 Apr 2020
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Neural Circuit Dynamics for Sensory Detection
Sruti Mallik, Srinath Nizampatnam, Anirban Nandi, Debajit Saha, Baranidharan Raman, ShiNung Ching
Journal of Neuroscience 22 April 2020, 40 (17) 3408-3423; DOI: 10.1523/JNEUROSCI.2185-19.2020

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Neural Circuit Dynamics for Sensory Detection
Sruti Mallik, Srinath Nizampatnam, Anirban Nandi, Debajit Saha, Baranidharan Raman, ShiNung Ching
Journal of Neuroscience 22 April 2020, 40 (17) 3408-3423; DOI: 10.1523/JNEUROSCI.2185-19.2020
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