Advertisement

BMC Neuroscience

, 16:P94 | Cite as

Analyzing adaptive modulation in spinal motor neurons using multi-objective evolutionary algorithms

  • Tomasz G Smolinski
  • Joseph Lombardo
  • Melissa A Harrington
Open Access
Poster presentation
  • 246 Downloads

Keywords

Network Activation Spinal Motoneuron Adaptive Modulation Relay System Persistent Activation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Spinal motoneurons have long been thought to be simply part of a relay system that provides rapid, stereotyped outputs for muscles on the basis of a supraspinal plan tuned by sensory inputs, and activity-dependent plasticity (ADP) has been presumed to be a property of the brain. However, recent work indicates that ADP occurs in spinal motor neurons during development, as well as later in life with skills acquisition and maintenance, and in response to trauma and disease [1]. Understanding how spinal motoneuron output can be modified by both increased and decreased activity is thus a fundamental challenge with implications for athletic training, rehabilitation, and advanced prosthetics. We hypothesize that that alteration in the function of Kv7.2 channel (which carries the M current) and changes in axonal initial segment (AIS) properties are the primary mechanisms of adaptation of spinal motoneurons to prolonged network activation. This hypothesis is supported by the literature and our experimental data demonstrating that persistent activation of spinal cord networks decreases spinal motoneuron output in a manner consistent with the enhancement of a sub-threshold, non-inactivating potassium conductance. KCNQ/Kv7 channels, which are non-inactivating potassium channels that activate in the sub-threshold range [2], are expressed at the AIS, nodes of Ranvier, and soma of spinal motoneurons [3, 4], and modulate their excitability [5, 6].

To test our hypothesis, we developed a realistic computational model of spinal motoneuron activity before and after persistent network activation. As the starting point, we utilized a reconstructed spinal motoneuron morphology of neonatal mice [7] together with the detailed specification of the active and passive somatodendritic and axonal properties derived from a rodent cortical neuron model [8]. The model parameter values were adjusted to match our recordings of motoneuron electrophysiological properties using a multi-objective evolutionary algorithm (MOEA) [9]. The algorithm matches multiple selection criteria simultaneously (e.g., spike frequency, shape, adaptation rate, etc.) and generates entire collections of neuronal models that can be mined for rules describing the phenomena captured by the models (for instance, co-regulations between ionic conductances). Furthermore, since the MOEA generates two independent databases of models (i.e., before and after persistent activation), we are able to directly compare the phenomena discovered by our data mining process in each dataset, thus elucidating the mechanisms underlying plasticity.

Notes

Acknowledgements

Support: NIH 5P20GM103653, NIH 1R15HD075207, NSF EPSCoR 0814251, NSF HRD1242067.

References

  1. 1.
    Wolpaw JR, Tennison AM: Activity-dependent spinal cord plasticity in health and disease. Annual Review of Neuroscience. 2001, 24: 807-843.PubMedCrossRefGoogle Scholar
  2. 2.
    Schroeder BC, et al: KCNQ5, a novel potassium channel broadly expressed in brain, mediates M-type currents. J Biol Chem. 2000, 275 (31): 24089-95.PubMedCrossRefGoogle Scholar
  3. 3.
    Dedek K, Waldegger S: Colocalization of KCNQ1/KCNE channel subunits in the mouse gastrointestinal tract. Pflugers Arch. 2001, 442 (6): 896-902.PubMedCrossRefGoogle Scholar
  4. 4.
    Devaux JJ, et al: KCNQ2 is a nodal K+ channel. J Neurosci. 2004, 24 (5): 1236-44.PubMedCrossRefGoogle Scholar
  5. 5.
    Alaburda A, Perrier JF, Hounsgaard J: An M-like outward current regulates the excitability of spinal motoneurones in the adult turtle. J Physiol. 2002, 540 (Pt 3): 875-81.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Rivera-Arconada I, Lopez-Garcia JA: Effects of M-current modulators on the excitability of immature rat spinal sensory and motor neurones. Eur J Neurosci. 2005, 22 (12): 3091-8.PubMedCrossRefGoogle Scholar
  7. 7.
    Li Y, et al: Developmental changes in spinal motoneuron dendrites in neonatal mice. J Comp Neurol. 2005, 483 (3): 304-17.PubMedCrossRefGoogle Scholar
  8. 8.
    Kole MH, et al: Action potential generation requires a high sodium channel density in the axon initial segment. Nat Neurosci. 2008, 11 (2): 178-86.PubMedCrossRefGoogle Scholar
  9. 9.
    Patel P, et al: Hybridization of multi-objective evolutionary algorithms and fuzzy control for automated construction, tuning, and analysis of neuronal models. BMC Neurosci. 2013, 14 (Suppl 1): P369-PubMedCentralCrossRefGoogle Scholar

Copyright information

© Smolinski et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Tomasz G Smolinski
    • 1
  • Joseph Lombardo
    • 2
  • Melissa A Harrington
    • 2
  1. 1.Department of Computer and Information SciencesDelaware State UniversityDoverUSA
  2. 2.Department of Biological SciencesDelaware State UniversityDoverUSA

Personalised recommendations