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From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals

Ferrario, Andrea, Palyanov, Andrey, Koutsikou, Stella, Li, Wenchang, Soffe, Stephen R., Roberts, Alan, Borisyuk, Roman (2021) From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals. PLoS Computational Biology, . ISSN 1553-734X. E-ISSN 1553-7358. (doi:10.1371/journal.pcbi.1009654) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91904)

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Abstract

How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The “Central Nervous System” (CNS) model uses evidence from whole-cell recording to define 2000 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D “Virtual Tadpole” (VT) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of “water”. We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.

Item Type: Article
DOI/Identification number: 10.1371/journal.pcbi.1009654
Uncontrolled keywords: motor decision, sensory memory, locomotion
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Q Science > QH Natural history > QH301 Biology
Q Science > QH Natural history > QH324.2 Computational biology
Divisions: Divisions > Division of Natural Sciences > Medway School of Pharmacy
Funders: Organisations -1 not found.
Organisations -1 not found.
Depositing User: Stella Koutsikou
Date Deposited: 02 Dec 2021 09:30 UTC
Last Modified: 16 Jan 2022 23:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91904 (The current URI for this page, for reference purposes)
Koutsikou, Stella: https://orcid.org/0000-0003-2933-3637
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