May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Propagation of Signals in the Network of Starburst Amacrine Cells
Author Affiliations & Notes
  • A. V. Dmitriev
    Dept of Neuroscience, Ohio State University, Columbus, Ohio
  • K. E. Gavrikov
    Dept of Neuroscience, Ohio State University, Columbus, Ohio
  • S. C. Mangel
    Dept of Neuroscience, Ohio State University, Columbus, Ohio
  • Footnotes
    Commercial Relationships  A.V. Dmitriev, None; K.E. Gavrikov, None; S.C. Mangel, None.
  • Footnotes
    Support  NIH Grant EY014235 to S.C.M.
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 3852. doi:https://doi.org/
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    • Get Citation

      A. V. Dmitriev, K. E. Gavrikov, S. C. Mangel; Propagation of Signals in the Network of Starburst Amacrine Cells. Invest. Ophthalmol. Vis. Sci. 2008;49(13):3852. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : Starburst amacrine cells (SACs) generate direction-selective (DS) light responses that strongly depend on lateral GABA-driven inputs (Gavrikov et. al., 2006), provided in part by other SACs (Lee, Zhou, 2006). Importantly, SACs respond to GABA with either a depolarization or hyperpolarization, depending on the local Cl- equilibrium potential (Gavrikov et.al. 2006). The finding that GABA can change the SAC membrane potential, and not just shunt the membrane, opens the unexplored possibility of lateral signal propagation through the net of densely packed SACs. In this work we have investigated how different Cl- distributions within SAC dendrites affect the ability of SACs to communicate with each other laterally and influence their DS responses.

Methods: : We computed a SAC network model in which SACs overlapped by a factor of 25. Each SAC was modeled by an electrical equivalent circuit that consisted of a cell body, and 8 proximal and 16 distal dendritic compartments. Each compartment except the cell body had a transmembrane resistance and a variable battery, whose potential depended on glutamate release from bipolar cells and GABA release from other SACs. Three computational steps in modeling the light responses of SACs were performed for each time frame (25 ms): 1) calculation of light-evoked glutamate-induced changes in membrane potential in each SAC compartment, 2) calculation of changes in local (75 x 75 µm area) synaptic GABA concentration due to changes in membrane potential at the distal dendrites, the site of GABA release, and 3) calculation of changes in membrane potential in each postsynaptic compartment of each modeled SAC due to changes in synaptic GABA.

Results: : The calculations showed that GABA-mediated lateral signal propagation in the network of SACs was possible only if changes in synaptic GABA were associated with changes in SAC membrane potential, i. e. if the local Cl- equilibrium potential was different from the local membrane potential. The best fit between the computational results and the experimentally measured light responses (Gavrikov et. al., 2006) was obtained when GABA hyperpolarized the distal dendrites, but depolarized the proximal dendrites. The calculations also showed that GABA-mediated signals in the SAC network could propagate for a longer distance than was previously proposed (Lee, Zhou, 2006).

Conclusions: : The SAC network is able to transfer signals from SAC to SAC using GABA, but only if there is a nonequilibrium distribution of Cl- within SAC dendrites. The simulation also supported the idea that intercommunication between neighboring SACs plays a key role in the generation of direction selectivity.

Keywords: amacrine cells • computational modeling • receptive fields 
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