Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Sensory Coding, Efficiency

  • Franz Weber
  • Christian K. Machens
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_325-1

Definition

If a neuron’s spikes are highly informative about an ensemble of stimuli, then the generated code is called efficient. The efficient coding hypothesis states that the highest levels of efficiency are reached when the ensemble of stimuli encoded by sensory neurons captures important aspects of an animal’s natural environment. This notion of efficiency has been employed to explain various properties of sensory neurons including their stimulus–response functions, gain, and connectivity. Specifically, research on insect systems has shown that the stimulus–response function of many insect sensory neurons matches behaviorally relevant stimuli, while the neural gain is adjusted to the current stimulus statistics and behavioral state.

Introduction

To probe the properties of sensory neurons, neurophysiologists present various stimuli (e.g., gratings with different orientations for visual neurons or tones with different frequencies for auditory neurons), while recording the neural...

Keywords

Mutual Information Sensory Neuron Spike Train Neural Response Antennal Lobe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61(3):183PubMedCrossRefGoogle Scholar
  2. Barlow HB (1961) Possible principles underlying the transformations of sensory messages. In Rosenblith WA (Ed.), Sensory communication, MIT Press, Cambridge, MA, pp 217-234Google Scholar
  3. Bialek W, Rieke F, de Ruyter van Steveninck R, Warland D (1991) Reading a neural code. Science 252:1854–1857PubMedCrossRefGoogle Scholar
  4. Borst A (2003) Noise, not stimulus entropy, determines neural information rate. J Comput Neurosci 14:23–31PubMedCrossRefGoogle Scholar
  5. Borst A, Haag J (2002) Neural networks in the cockpit of the fly. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 188:419–437PubMedCrossRefGoogle Scholar
  6. Borst A, Theunissen FE (1999) Information theory and neural coding. Nat Neurosci 2:947–957PubMedCrossRefGoogle Scholar
  7. Borst A, Flanagin VL, Sompolinsky H (2005) Adaptation without parameter change: dynamic gain control in motion detection. Proc Natl Acad Sci USA 102:6172–6176PubMedCentralPubMedCrossRefGoogle Scholar
  8. Brenner N, Bialek W, de Ruyter van Steveninck R (2000) Adaptive rescaling maximizes information transmission. Neuron 26:695–702PubMedCrossRefGoogle Scholar
  9. Broome BM, Jayaraman V, Laurent G (2006) Encoding and decoding of overlapping odor sequences. Neuron 51:467–482PubMedCrossRefGoogle Scholar
  10. Brunel N, Nadal J-P (1998) Mutual information, Fisher information, and population coding. Neural Comput 10:1731–1757PubMedCrossRefGoogle Scholar
  11. Chiappe ME, Seelig JD, Reiser MB, Jayaraman V (2010) Walking modulates speed sensitivity in Drosophila motion vision. Curr Biol 20:1470–1475PubMedCrossRefGoogle Scholar
  12. Fairhall AL, Lewen GD, Bialek W, De Ruyter Van Steveninck RR (2001) Efficiency and ambiguity in an adaptive neural code. Nature 412:787–792PubMedCrossRefGoogle Scholar
  13. Faisal AA, Selen LPJ, Wolpert DM (2008) Noise in the nervous system. Nat Rev Neurosci 9:292–303PubMedCentralPubMedCrossRefGoogle Scholar
  14. Gabbiani F, Metzner W (1999) Encoding and processing of sensory information in neuronal spike trains. J Exp Biol 202(10):1267–1279PubMedGoogle Scholar
  15. Geffen MN, Broome BM, Laurent G, Meister M (2009) Neural encoding of rapidly fluctuating odors. Neuron 61:570–586PubMedCrossRefGoogle Scholar
  16. Jung SN, Borst A, Haag J (2011) Flight activity alters velocity tuning of fly motion-sensitive neurons. J Neurosci 31:9231–9237PubMedCrossRefGoogle Scholar
  17. Laughlin S (1981) A simple coding procedure enhances a neuron’s information capacity. Z Naturforsch C 36:910–912PubMedGoogle Scholar
  18. Longden KD, Krapp HG (2009) State-dependent performance of optic-flow processing interneurons. J Neurophysiol 102:3606–3618PubMedCrossRefGoogle Scholar
  19. Longden KD, Krapp HG (2010) Octopaminergic modulation of temporal frequency coding in an identified optic flow-processing interneuron. Front Syst Neurosci 4:153PubMedCentralPubMedCrossRefGoogle Scholar
  20. Machens CK, Stemmler MB, Prinz P, Krahe R, Ronacher B, Herz AV (2001) Representation of acoustic communication signals by insect auditory receptor neurons. J Neurosci 21:3215–3227PubMedGoogle Scholar
  21. Machens CK, Gollisch T, Kolesnikova O, Herz AVM (2005) Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron 47:447–456PubMedCrossRefGoogle Scholar
  22. Maimon G, Straw AD, Dickinson MH (2010) Active flight increases the gain of visual motion processing in Drosophila. Nat Neurosci 13:393–399PubMedCrossRefGoogle Scholar
  23. Olsen SR, Wilson RI (2008) Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452:956–960PubMedCentralPubMedCrossRefGoogle Scholar
  24. Olsen SR, Bhandawat V, Wilson RI (2010) Divisive normalization in olfactory population codes. Neuron 66:287–299PubMedCentralPubMedCrossRefGoogle Scholar
  25. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609PubMedCrossRefGoogle Scholar
  26. Paninski L (2003) Estimation of entropy and mutual information. Neural Comput 15:1191–1253CrossRefGoogle Scholar
  27. Rieke F, Bialek W, Warland D (1997) Spikes: Exploring the Neural Code. MIT Press, CambridgeGoogle Scholar
  28. Rinberg D, Davidowitz H (2000) Do cockroaches ‘know’ about fluid dynamics? Nature 405:756PubMedCrossRefGoogle Scholar
  29. Root CM, Ko KI, Jafari A, Wang JW (2011) Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. Cell 145:133–144PubMedCentralPubMedCrossRefGoogle Scholar
  30. Sengupta P (2013) The belly rules the nose: feeding state-dependent modulation of peripheral chemosensory responses. Curr Opin Neurobiol 23:68–75PubMedCentralPubMedCrossRefGoogle Scholar
  31. Simoncelli EP (2003) Vision and the statistics of the visual environment. Curr Opin Neurobiol 13(2):144–149PubMedCrossRefGoogle Scholar
  32. Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Ann Rev Neurosci 24:1193–1216PubMedCrossRefGoogle Scholar
  33. Smith EC, Lewicki MS (2006) Efficient auditory coding. Nature 439:978–982PubMedCrossRefGoogle Scholar
  34. Strong SP, Koberle R, de Ruyter van Steveninck R, Bialek W (1998) Entropy and information in neural spike trains. Phys Rev Lett 80:197CrossRefGoogle Scholar
  35. Theunissen FE, Miller JP (1991) Representation of sensory information in the cricket cercal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons. J Neurophysiol 66:1690–1703PubMedGoogle Scholar
  36. Van Hateren JH (1992) Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation. J Comp Physiol A 171:157–170CrossRefGoogle Scholar
  37. Wark B, Lundstrom BN, Fairhall A (2007) Sens Adapt Curr Opin Neurobiol 17:423–429CrossRefGoogle Scholar
  38. Weber F, Machens CK, Borst A (2010) Spatiotemporal response properties of optic-flow processing neurons. Neuron 67:629–642PubMedCrossRefGoogle Scholar
  39. Weber F, Machens CK, Borst A (2012) Disentangling the functional consequences of the connectivity between optic-flow processing neurons. Nat Neurosci 15:441–448, S1–S2PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Molecular and Cell BiologyUniversity of CaliforniaBerkeleyUSA