Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly

Karmeier K, Krapp HG, Egelhaaf M (2005)
Journal of neurophysiology 94(3): 2182-2194.

Download
OA
Zeitschriftenaufsatz | Veröffentlicht | Englisch
Autor
; ;
Abstract / Bemerkung
Coding of sensory information often involves the activity of neuronal populations. We demonstrate how the accuracy of a population code depends on integration time, the size of the population, and noise correlation between the participating neurons. The population we study consists of 10 identified visual interneurons in the blowfly Calliphora vicina involved in optic flow processing. These neurons are assumed to encode the animal's head or body rotations around horizontal axes by means of graded potential changes. From electrophysiological experiments we obtain parameters for modeling the neurons' responses. From applying a Bayesian analysis on the modeled population response we draw three major conclusions. First, integration of neuronal activities over a time period of only 5 ms after response onset is sufficient to decode accurately the rotation axis. Second, noise correlation between neurons has only little impact on the population's performance. And third, although a population of only two neurons would be sufficient to encode any horizontal rotation axis, the population of 10 vertical system neurons is advantageous if the available integration time is short. For the fly, short integration times to decode neuronal responses are important when controlling rapid flight maneuvers.
Erscheinungsjahr
Zeitschriftentitel
Journal of neurophysiology
Band
94
Zeitschriftennummer
3
Seite
2182-2194
ISSN
eISSN
PUB-ID

Zitieren

Karmeier K, Krapp HG, Egelhaaf M. Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly. Journal of neurophysiology. 2005;94(3):2182-2194.
Karmeier, K., Krapp, H. G., & Egelhaaf, M. (2005). Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly. Journal of neurophysiology, 94(3), 2182-2194. doi:10.1152/jn.00278.2005
Karmeier, K., Krapp, H. G., and Egelhaaf, M. (2005). Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly. Journal of neurophysiology 94, 2182-2194.
Karmeier, K., Krapp, H.G., & Egelhaaf, M., 2005. Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly. Journal of neurophysiology, 94(3), p 2182-2194.
K. Karmeier, H.G. Krapp, and M. Egelhaaf, “Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly”, Journal of neurophysiology, vol. 94, 2005, pp. 2182-2194.
Karmeier, K., Krapp, H.G., Egelhaaf, M.: Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly. Journal of neurophysiology. 94, 2182-2194 (2005).
Karmeier, Katja, Krapp, Holger G., and Egelhaaf, Martin. “Population coding of self-motion: applying Bayesian analysis to a population of visual interneurons in the fly”. Journal of neurophysiology 94.3 (2005): 2182-2194.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2016-09-28T07:22:31Z

15 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Subcellular mapping of dendritic activity in optic flow processing neurons.
Hopp E, Borst A, Haag J., J Comp Physiol A Neuroethol Sens Neural Behav Physiol 200(5), 2014
PMID: 24647929
Near-optimal decoding of transient stimuli from coupled neuronal subpopulations.
Trousdale J, Carroll SR, Gabbiani F, Josić K., J Neurosci 34(36), 2014
PMID: 25186763
Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system.
Meckenhäuser G, Krämer S, Farkhooi F, Ronacher B, Nawrot MP., Front Syst Neurosci 8(), 2014
PMID: 25565983
Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action.
Egelhaaf M, Boeddeker N, Kern R, Kurtz R, Lindemann JP., Front Neural Circuits 6(), 2012
PMID: 23269913
Efficient Markov chain Monte Carlo methods for decoding neural spike trains.
Ahmadian Y, Pillow JW, Paninski L., Neural Comput 23(1), 2011
PMID: 20964539
Fly motion vision.
Borst A, Haag J, Reiff DF., Annu Rev Neurosci 33(), 2010
PMID: 20225934
Local and global motion preferences in descending neurons of the fly.
Wertz A, Haag J, Borst A., J Comp Physiol A Neuroethol Sens Neural Behav Physiol 195(12), 2009
PMID: 19830435
Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly.
Lindemann JP, Weiss H, Möller R, Egelhaaf M., Biol Cybern 98(3), 2008
PMID: 18180948
Visuomotor transformation in the fly gaze stabilization system.
Huston SJ, Krapp HG., PLoS Biol 6(7), 2008
PMID: 18651791
Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons.
Cuntz H, Haag J, Forstner F, Segev I, Borst A., Proc Natl Acad Sci U S A 104(24), 2007
PMID: 17551009
Encoding of naturalistic optic flow by a population of blowfly motion-sensitive neurons.
Karmeier K, van Hateren JH, Kern R, Egelhaaf M., J Neurophysiol 96(3), 2006
PMID: 16687623

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 15901759
PubMed | Europe PMC

Suchen in

Google Scholar