Gating of steering signals through phasic modulation of reticulospinal neurons during locomotion
2014 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 111, no 9, 3591-3596 p.Article in journal (Refereed) Published
The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem-spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on-or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem-spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.
Place, publisher, year, edition, pages
2014. Vol. 111, no 9, 3591-3596 p.
large-scale modeling, compartmental modelling, full-scale model, MLR
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:kth:diva-144949DOI: 10.1073/pnas.1401459111ISI: 000332560300085ScopusID: 2-s2.0-84895795088OAI: oai:DiVA.org:kth-144949DiVA: diva2:715456
FunderSwedish Research CouncilSwedish e‐Science Research Center
QC 201405052014-05-052014-05-052014-05-05Bibliographically approved