Sensory adaptation

Current Opinion in Neurobiology

Volumn 17, Issue 4, 2007, Pages 423-429

  Wark, Barry ^a   Lundstrom, Brian Nils ^a   Fairhall, Adrienne ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION; AUDITORY CORTEX; BRAIN REGION; HUMAN; NERVE CELL; NERVE CELL NETWORK; NERVE STIMULATION; NOISE; NONHUMAN; NONLINEAR SYSTEM; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; REVIEW; SENSORY SYSTEM; THEORETICAL MODEL;

ADAPTATION, PHYSIOLOGICAL; ANIMALS; HUMANS; NERVE NET; SENSATION;

EID: 34948900353     PISSN: 09594388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conb.2007.07.001     Document Type: Review

References (65)

1. Simoncelli E.P. Vision and the statistics of the visual environment. Curr Opin Neurobiol 13 (2003) 144-149
2. Barlow H.B. Possible principles underlying the transformation of sensory messages. In: Rosenblith W. (Ed). Sensory Communication (1961), MIT Press
3. Laughlin S.B. The role of sensory adaptation in the retina. J Exp Biol 146 (1989) 39-62
4. Ulanovsky N., Las L., and Nelken I. Processing of low-probability sounds by cortical neurons. Nat Neurosci 6 (2003) 391-398
5. Ulanovsky N., Las L., Farkas D., and Nelken I. Multiple time scales of adaptation in auditory cortex neurons. J Neurosci 24 (2004) 10440-10453
6. Eytan D., Brenner N., and Marom S. Selective adaptation in networks of cortical neurons. J Neurosci 23 (2003) 9349-9356
7. Simoncelli E.P., and Olshausen B.A. Natural image statistics and neural representation. Annu Rev Neurosci 24 (2001) 1193-1216
8. Ruderman D.L., and Bialek W. Statistics of natural images: scaling in the woods. Phys Rev Lett 73 (1994) 814-817
9. Smirnakis S.M., Berry M.J., Warland D.K., Bialek W., and Meister M. Adaptation of retinal processing to image contrast and spatial scale. Nature 386 (1997) 69-73
10. Brenner N., Bialek W., and de Ruyter van Steveninck R. Adaptive rescaling maximizes information transmission. Neuron 26 (2000) 695-702
11. Fairhall A.L., Lewen G.D., Bialek W., and de Ruyter van Steveninck R.R. Efficiency and ambiguity in an adaptive neural code. Nature 412 (2001) 787-792
12. Shapley R.M., and Victor J.D. The effect of contrast on the transfer properties of cat retinal ganglion cells. J Physiol 285 (1978) 275-298
13. Kim K.J., and Rieke F. Temporal contrast adaptation in the input and output signals of salamander retinal ganglion cells. J Neurosci 21 (2001) 287-299
14. Baccus S.A., and Meister M. Fast and slow contrast adaptation in retinal circuitry. Neuron 36 (2002) 909-919
15. Chander D., and Chichilnisky E.J. Adaptation to temporal contrast in primate and salamander retina. J Neurosci 21 (2001) 9904-9916
16. Zaghloul K.A., Boahen K., and Demb J.B. Contrast adaptation in subthreshold and spiking responses of mammalian Y-type retinal ganglion cells. J Neurosci 25 (2005) 860-868
17. Maravall M., Petersen R.S., Fairhall A.L., Arabzadeh E., and Diamond M.E. Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex. PLoS Biol 5 (2007) e19. It is shown that adaptation to stimulus variance also occurs in neurons in primary somatosensory cortex; here, rat barrel cortex. Because of local phase invariance of the response, features and gain curves were extracted using covariance analysis. While features did not change with variance, the ranges of the gain curves were found to scale proportionally to the stimulus standard deviation in the majority of neurons, maintaining information transmission.
18. Nagel K.I., and Doupe A.J. Temporal processing and adaptation in the songbird auditory forebrain. Neuron 51 (2006) 845-859. The responses of neurons in avian field L are analyzed during changes in mean and variance of the randomly varying amplitude of a broadband noise input. The filters of an LN model change systematically with changes in the mean. As in [11,14], while the firing rate changes slowly following a change in stimulus variance, the filters and gain curves change nearly instantaneously.
19. Dean I., Harper N.S., and McAlpine D. Neural population coding of sound level adapts to stimulus statistics. Nat Neurosci 8 (2005) 1684-1689
20. De Baene W., Premereur E., and Vogels R. Properties of shape tuning of macaque inferior temporal neurons examined using Rapid Serial Visual Presentation. J Neurophysiol (2007)
21. Mante V., Frazor R.A., Bonin V., Geisler W.S., and Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8 (2005) 1690-1697
22. Bonin V., Mante V., and Carandini M. The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25 (2005) 10844-10856
23. Hosoya T., Baccus S.A., and Meister M. Dynamic predictive coding by the retina. Nature 436 (2005) 71-77. This study explores adaptation to complex spatial or temporal correlations in salamander and rabbit retina, showing that the receptive fields of many RGCs change following stimulation with these correlated stimuli such that the response to novel stimuli is enhanced compared with the response to the adapting stimulus. The authors reject the hypothesis of fatigue of pattern matching cells in favor of a model of Hebbian plasticity at inhibitory amacrine-to-RGC synapses. This conclusion is intriguingly at odds with other studies that find adaptation in the retina to simpler correlations (mean, variance) in the absence of inhibitory transmission [34].
24. Srinivasan M.V., Laughlin S.B., and Dubs A. Predictive coding: a fresh view of inhibition in the retina. Proc R Soc Lond B Biol Sci 216 (1982) 427-459
25. Atick. Could information theory provide an ecological theory of sensory processing?. Network 3 (1992) 213-251
26. Simoncelli E.P., Paninski L., Pillow J., and Schwartz O. Characterization of neural responses with stochastic stimuli. In: Gazzaniga M. (Ed). The New Cognitive Neurosciences. edn 3 (2004), MIT Press 327-338
27. Theunissen F.E., Sen K., and Doupe A.J. Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci 20 (2000) 2315-2331
28. Woolley S.M., Gill P.R., and Theunissen F.E. Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain. J Neurosci 26 (2006) 2499-2512
29. David S.V., Vinje W.E., and Gallant J.L. Natural stimulus statistics alter the receptive field structure of v1 neurons. J Neurosci 24 (2004) 6991-7006
30. Victor J.D., Mechler F., Repucci M.A., Purpura K.P., and Sharpee T. Responses of V1 neurons to two-dimensional hermite functions. J Neurophysiol 95 (2006) 379-400
31. Sharpee T., Rust N.C., and Bialek W. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput 16 (2004) 223-250
32. Sharpee T.O., Sugihara H., Kurgansky A.V., Rebrik S.P., Stryker M.P., and Miller K.D. Adaptive filtering enhances information transmission in visual cortex. Nature 439 (2006) 936-942. This paper applies a novel information theoretic reverse correlation method [31] to obtain cat V1 receptive fields during viewing of a white noise stimulus and of natural movies. The receptive fields differed in the two cases in their low-pass properties, in such a way as to transmit similar low frequency power in the two cases. The information transmitted about the stimulus was determined to change on the order of ∼100 s.
33. Dunn F.A., and Rieke F. The impact of photoreceptor noise on retinal gain controls. Curr Opin Neurobiol 16 (2006) 363-370
34. Brown S.P., and Masland R.H. Spatial scale and cellular substrate of contrast adaptation by retinal ganglion cells. Nat Neurosci 4 (2001) 44-51
35. Manookin M.B., and Demb J.B. Presynaptic mechanism for slow contrast adaptation in mammalian retinal ganglion cells. Neuron 50 (2006) 453-464. This study adds to our understanding of the relationship between network and intrinsic components of temporal contrast adaptation in retina. The authors conclude that recovery from a high contrast stimulus, characterized by a slow afterhyperpolarization in guinea pig RGC membrane potential and firing rate, is inherited largely from presynaptic mechanisms. In addition, they find a small contribution of intrinsic mechanisms in the RGC. This work is consistent with previous findings in salamander retina [13,61].
36. Thorson J., and Biederman-Thorson M. Distributed relaxation processes in sensory adaptation. Science 183 (1974) 161-172
37. Webber R.M., and Stanley G.B. Transient and steady-state dynamics of cortical adaptation. J Neurophysiol 95 (2006) 2923-2932
38. Borst A., Flanagin V.L., and Sompolinsky H. Adaptation without parameter change: dynamic gain control in motion detection. Proc Natl Acad Sci U S A 102 (2005) 6172-6176. In the fly visual system, previous work has demonstrated that the H1 neuron adapts its input/output relationship as stimulus statistics change. These authors find that this seemingly complicated adaptation can be explained by the intrinsic properties of a Reichhardt correlator motion detector, where such rescaling is a consequence of the system's nonlinearity acting on a multidimensional stimulus. This gain change appears in a reduced dimensional model as a result of other, correlated, dimensions of the stimulus that are not included in the LN model yet affect the response differently at different variances.
39. Brunel N., Chance F.S., Fourcaud N., and Abbott L.F. Effects of synaptic noise and filtering on the frequency response of spiking neurons. Phys Rev Lett 86 (2001) 2186-2189
40. Fourcaud-Trocme N., Hansel D., van Vreeswijk C., and Brunel N. How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci 23 (2003) 11628-11640
41. Yu Y., and Lee T.S. Dynamical mechanisms underlying contrast gain control in single neurons. Phys Rev E Stat Nonlin Soft Matter Phys 68 (2003) 011901
42. Paninski L., Lau B., and Reyes A. Noise-driven adaptation: in vitro and mathematical analysis. Neurocomputing (2003) 877-883
43. Hong S, Agüera y Arcas B, Fairhall AL: Single neuron computation: from dynamical system to feature detector. Neural Comput 2007, in press.
44. Rudd M.E., and Brown L.G. Noise adaptation in integrate-and fire neurons. Neural Comput 9 (1997) 1047-1069
45. La Camera G., Rauch A., Thurbon D., Luscher H.R., Senn W., and Fusi S. Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons. J Neurophysiol 96 (2006) 3448-3464
46. Liu Y.H., and Wang X.J. Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. J Comput Neurosci 10 (2001) 25-45
47. Wang X.J. Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J Neurophysiol 79 (1998) 1549-1566
48. Benda J., and Herz A.V. A universal model for spike-frequency adaptation. Neural Comput 15 (2003) 2523-2564
49. Benda J., Longtin A., and Maler L. Spike-frequency adaptation separates transient communication signals from background oscillations. J Neurosci 25 (2005) 2312-2321
50. Arsiero M., Luscher H.R., Lundstrom B.N., and Giugliano M. The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. J Neurosci 27 (2007) 3274-3284
51. Higgs M.H., Slee S.J., and Spain W.J. Diversity of gain modulation by noise in neocortical neurons: regulation by the slow afterhyperpolarization conductance. J Neurosci 26 (2006) 8787-8799
52. Prescott S.A., Ratte S., De Koninck Y., and Sejnowski T.J. Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. J Neurosci 26 (2006) 9084-9097
53. Fairhall A.L., Lewen G.D., Bialek W., and de Ruyter van Steveninck R. Multiple timescales of adaptation in a neural code. In: Leen T.K., Dietterich T.G., and Tresp V. (Eds). Advances in Neural Information Processing Systems 13 (2001), MIT Press 124-130
54. Drew P.J., and Abbott L.F. Models and properties of power-law adaptation in neural systems. J Neurophysiol 96 (2006) 826-833
55. Gilboa G., Chen R., and Brenner N. History-dependent multiple-time-scale dynamics in a single-neuron model. J Neurosci 25 (2005) 6479-6489. Following the observation of Toib, Lyakhov, and Marom [56] that mammalian brain sodium channels display power-law-like recovery from inactivation, this article models sodium channel inactivation using a Markov chain of one activation state and many inactivation states, where neuronal excitability is related to the fraction of available channels. The characteristic time scale of adaptation is found to depend on stimulus duration by a power-law scaling. The key aspect of this model is that the time scale of recovery depends on how its large pool of degenerate inactive states is populated, which in turn is affected by stimulus history.
56. + channels. J Neurosci 18 (1998) 1893-1903
57. Schwartz O., and Simoncelli E.P. Natural signal statistics and sensory gain control. Nat Neurosci 4 (2001) 819-825
58. Carandini M., Heeger D.J., and Movshon J.A. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17 (1997) 8621-8644
59. Sanchez-Vives M.V., Nowak L.G., and McCormick D.A. Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro. J Neurosci 20 (2000) 4286-4299
60. Rieke F. Temporal contrast adaptation in salamander bipolar cells. J Neurosci 21 (2001) 9445-9454
61. + inactivation and variance adaptation in salamander retinal ganglion cells. J Neurosci 23 (2003) 1506-1516
62. Dunn F.A., Doan T., Sampath A.P., and Rieke F. Controlling the gain of rod-mediated signals in the Mammalian retina. J Neurosci 26 (2006) 3959-3970
63. Katz Y., Heiss J.E., and Lampl I. Cross-whisker adaptation of neurons in the rat barrel cortex. J Neurosci 26 (2006) 13363-13372
64. Laughlin S. A simple coding procedure enhances a neuron's information capacity. Z Naturforsch [C] 36 (1981) 910-912
65. Stanley G.B. Adaptive spatiotemporal receptive field estimation in the visual pathway. Neural Comput 14 (2002) 2925-2946