JacksonCha2023 ICB-DCM#1450

dweindl · Sep 6, 2024 · 730659c · 730659c
1 parent 53a5224
commit 730659c
Showing 1 changed file with 16 additions and 0 deletions.
diff --git a/doc/using_pypesto.bib b/doc/using_pypesto.bib
@@ -321,4 +321,20 @@ @Article{SchmiesterBra2024
   url              = {https://doi.org/10.1158/1078-0432.CCR-24-0244},
 }
 
+@InProceedings{JacksonCha2023,
+  author           = {Jackson, Clayton and Chardon, Matthieu and Wang, Y. Curtis and Rudi, Johann and Tresch, Matthew and Heckman, Charles J. and Quinn, Roger D.},
+  booktitle        = {Biomimetic and Biohybrid Systems},
+  title            = {Multimodal Parameter Inference for a Canonical Motor Microcircuit Controlling Rat Hindlimb Motion},
+  year             = {2023},
+  address          = {Cham},
+  editor           = {Meder, Fabian and Hunt, Alexander and Margheri, Laura and Mura, Anna and Mazzolai, Barbara},
+  pages            = {38--51},
+  publisher        = {Springer Nature Switzerland},
+  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.},
+  creationdate     = {2024-09-06T15:49:21},
+  doi              = {10.1007/978-3-031-39504-8_3},
+  isbn             = {978-3-031-39504-8},
+  modificationdate = {2024-09-06T15:49:47},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}