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Multimodal Parameter Inference for a Canonical Motor Microcircuit Controlling Rat Hindlimb Motion

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Biomimetic and Biohybrid Systems (Living Machines 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14158))

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Abstract

This work explored synaptic strengths in a computational neuroscience model of a controller for the hip joint of a rat which consists of Ia interneurons, Renshaw cells, and the associated motor neurons. This circuit has been referred to as the Canonical Motor Microcircuit (CMM). It is thought that the CMM acts to modulate motor neuron activity at the output stage. We first created a biomechanical model of a rat hindlimb consisting of a pelvis, femur, shin, foot, and flexor-extensor muscle pairs modeled with a Hill muscle model. We then modeled the CMM using non-spiking leaky-integrator neural models connected with conductance-based synapses. To tune the parameters in the network, we implemented an automated approach for parameter search using the Markov chain Monte Carlo (MCMC) method to solve a parameter estimation problem in a Bayesian inference framework. As opposed to traditional optimization techniques, the MCMC method identifies probability densities over the multidimensional space of parameters. This allows us to see a range of likely parameters that produce model outcomes consistent with animal data, determine if the distribution of likely parameters is uni- or multi-modal, as well as evaluate the significance and sensitivity of each parameter. This approach will allow for further analysis of the circuit, specifically, the function and significance of Ia feedback and Renshaw cells.

This work was supported by NSF DBI 2015317 as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106-7222, USA

    Clayton Jackson & Roger D. Quinn

  2. Interdepartmental Neuroscience, Northwestern University, Chicago, IL, USA

    Matthieu Chardon

  3. Department of Electrical and Computer Engineering, California State University, Los Angeles, CA, 90032, USA

    Y. Curtis Wang

  4. Department of Mathematics, Virginia Tech, Blacksburg, VA, 24060, USA

    Johann Rudi

  5. Department of Biomedical Engineering Northwestern University, Evanston, IL, 60208, USA

    Matthew Tresch

  6. Department of Physical Therapy and Human Movement Sciences, Northwestern University, Evanston, IL, 60208, USA

    Charles J. Heckman

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  1. Clayton Jackson
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Correspondence to Clayton Jackson .

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Editors and Affiliations

  1. Italian Institute of Technology, Genova, Italy

    Fabian Meder

  2. Portland State University, Portland, OR, USA

    Alexander Hunt

  3. Istituto Italiano di Tecnologia, Genova, Italy

    Laura Margheri

  4. Radboud University Nijmegen, Barcelona, Barcelona, Spain

    Anna Mura

  5. Italian Institute of Technology, Genova, Italy

    Barbara Mazzolai

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Jackson, C. et al. (2023). Multimodal Parameter Inference for a Canonical Motor Microcircuit Controlling Rat Hindlimb Motion. In: Meder, F., Hunt, A., Margheri, L., Mura, A., Mazzolai, B. (eds) Biomimetic and Biohybrid Systems. Living Machines 2023. Lecture Notes in Computer Science(), vol 14158. Springer, Cham. https://doi.org/10.1007/978-3-031-39504-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-39504-8_3

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