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  • Poster Presentation
  • Open Access

Network model for visually mediated ciliary locomotion in Hermissenda

BMC Neuroscience201011 (Suppl 1) :P59

  • Published:


  • Nervous System
  • Animal Model
  • Empirical Study
  • Network Model
  • Sensory Input
The overall goal of this study is to investigate the ways in which learning modifies behavior. A combination of computational and empirical studies is being used to address this issue. Empirical studies investigate learning from a cellular and synaptic perspective in the relatively simple nervous system of the nudibranch mollusk Hermissenda[13]. Pavlovian conditioning produces light-elicited inhibition of normal positive phototaxis in Hermissenda. Learning changes both cellular excitability and synaptic strength in the neural circuit that supports phototaxis. In the present study, a model of the circuit that supports visually mediated locomotion (Fig. 1A) was developed. Consistent with empirical observations, simulated responses to light increased the level of VP1 spike activity (Fig. 1B1), which is equivalent to positive phototaxis. Simulations indicated that phototaxis resulted from disinhibition of VP1. Light increased activity in Ie and decreased activity in Ii (Fig. 1B2). The net result was less activity in IIIi and disinhibition of VP1 (Fig. 1B2). Simulations also indicated that disinhibition produced phototaxis only if VP1 had a high level of tonic firing. The model is being refined and expanded, and will be used to investigate the generation of other behaviors (e.g., foot contraction), the responses to other sensory inputs (e.g., gravity), and the influence of learning-induced plasticity (e.g., increased Ie excitability and decreased VP1 tonic firing). Simulations also will help identify features of the model that warrant further empirical investigation.
Figure 1
Figure 1

Network model for visually mediated ciliary locomotion. A: The model had six cells: LB, lateral B-type photoreceptor; Ib, Ie, Ii and IIIi, interneurons; VP1, ciliary motor neuron. The cells were Hodgkin-Huxley-like neurons. Membrane conductances also included noise. The match between empirical data and model properties was qualitative. For example, all synaptic connections were modeled as monosynaptic, which reflected ‘functional’ connections. The circuit was implemented in SNNAP [4]. B1: The model responded to light, which was simulated by depolarizing LB, with increased VP1 activity. B2: The mean (+/- SD; n = 7) spiking frequency (Hz) of each cell before (dark) and during the light stimulus (light). Noise produced variability among simulations. In some cases, the error bars were not visible.



This work was supported by NIH grants P01 NS038310 and R01 MH058698.

Authors’ Affiliations

Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, TX 77030, USA


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© Baxter and Crow; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.