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Near-Optimal Decoding of Transient Stimuli from Coupled Neuronal Subpopulations

MPG-Autoren
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Gabbiani,  Fabrizio
Department: Circuits-Computation-Models / Borst, MPI of Neurobiology, Max Planck Society;

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Zitation

Trousdale, J., Carroll, S. R., Gabbiani, F., & Josic, K. (2014). Near-Optimal Decoding of Transient Stimuli from Coupled Neuronal Subpopulations. JOURNAL OF NEUROSCIENCE, 34(36), 12206-12222. doi:10.1523/JNEUROSCI.2671-13.2014.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0023-F98D-C
Zusammenfassung
Coupling between sensory neurons impacts their tuning properties and correlations in their responses. How such coupling affects sensory representations and ultimately behavior remains unclear. We investigated the role of neuronal coupling during visual processing using a realistic biophysical model of the vertical system (VS) cell network in the blow fly. These neurons are thought to encode the horizontal rotation axis during rapid free-flight maneuvers. Experimental findings suggest that neurons of the VS are strongly electrically coupled, and that several downstream neurons driving motor responses to ego-rotation receive inputs primarily from a small subset of VS cells. These downstream neurons must decode information about the axis of rotation from a partial readout of the VS population response. To investigate the role of coupling, we simulated the VS response to a variety of rotating visual scenes and computed optimal Bayesian estimates from the relevant subset of VS cells. Our analysis shows that coupling leads to near-optimal estimates from a subpopulation readout. In contrast, coupling between VS cells has no impact on the quality of encoding in the response of the full population. We conclude that coupling at one level of the fly visual system allows for near-optimal decoding from partial information at the subsequent, premotor level. Thus, electrical coupling may provide a general mechanism to achieve near-optimal information transfer from neuronal subpopulations across organisms and modalities.