- Oral presentation
- Open Access
Closing the loop: optimal stimulation of C. elegans neuronal network via adaptive control to exhibit full body movements
© Santos and Shlizerman 2015
- Published: 4 December 2015
- Motor Neuron
- Sensory Neuron
- Hopf Bifurcation
- Adaptive Control
- Sensory Input
The Caenorhabditis elegans (C. elegans) worm is a well-studied biological organism model. The nervous system of C. elegans is particularly appealing to study, since it is a tractable fully functional neuronal network for which electro-physical connectivity map (connectome) is fully resolved [1, 2]. In a recent work, we succeeded in establishing a computational dynamical model for the C. elegans nervous system and showed that robust oscillatory movements in motor neurons along the body can be invoked by constant current excitation of command sensory neurons (e.g. PLM neurons associated with forward crawling) and that their activation corresponds to low-dimensional Hopf bifurcation . While these first results validated the model, it is exciting to learn how the nervous system transforms its oscillatory dynamics to the muscles to support robust full body movements (e.g. forward crawling) . Moreover, using methods generically applicable to other neuronal circuits, it is intriguing to understand the optimal sensory stimulations that cause these movements to persist.
We utilize our computational full body model to determine the appropriate sensory input for behavior, such as crawling, to persist after explicit external stimulation (touch) has ceased, as observed in experiments . Since such persistence could be explained by a feedback loop between the environment and sensory neurons (Fig. 1C), we propose an adaptive control algorithm that extends existing recursive least squares-based algorithms (e.g. FORCE ). Our algorithm finds weights for synaptic input using a low-dimensional projection of motor neuron dynamics, and is capable of finding sensory input patterns that will lead to the desired movement.
- Varshney LR, Chen BL, Paniagua E, Hall DH, Chklovskii DB: Structural Properties of the Caenorhabditis elegans Neuronal Network. PLoS Comput Biol. 2011, 7 (2): 1001066-View ArticleGoogle Scholar
- Sengupta P, Samuel ADT: Caenorhabditis elegans: a model system for systems neuroscience. Curr Opin Neurobiol. 2009, 19 (6): 1-7.View ArticleGoogle Scholar
- Kunert J, Shlizerman E, Kutz JN: Low-dimensional functionality of complex network dynamics: Neurosensory integration in the Caenorhabditis elegans connectome. Phys Rev E. 2014, 89 (5): 052805-View ArticleGoogle Scholar
- McMillen T, Williams T, Holmes P: Nonlinear Muscles, Passive Viscoelasticity and Body Taper Conspire To Create Neuromechanical Phase Lags in Anguilliform Swimmers. PLoS Comput Biol. 2008, 4 (8): 1000157-View ArticleGoogle Scholar
- Backholm M, Ryu WS, Dalnoki-Veress K: Viscoelastic properties of the nematode Caenorhabditis elegans, a self-similar, shear-thinning worm. PNAS. 2013, 110 (12): 4528-4533.PubMedPubMed CentralView ArticleGoogle Scholar
- Sussillo D, Abbott LF: Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron. 2009, 63 (4): 544-557.PubMedPubMed CentralView ArticleGoogle Scholar
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