Associative learning enhances population coding by inverting interneuronal correlation patterns

Neuron

Volumn 78, Issue 2, 2013, Pages 352-363

  Jeanne, James M ^a,b,d   Sharpee, Tatyana O ^a,d   Gentner, Timothy Q ^a,b,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d SALK INSTITUTE FOR BIOLOGICAL STUDIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NERVE FIBER; ANIMAL CELL; ARTICLE; AUDITORY STIMULATION; BEHAVIORAL SCIENCE; BIOPHYSICS; BRAIN; BRAIN CELL; CAUDOLATERAL MESOPALLIUM; CELL POPULATION; CONTROLLED STUDY; EVOKED AUDITORY RESPONSE; INTERNEURON; NERVE CELL MEMBRANE POTENTIAL; NERVE CELL PLASTICITY; NONHUMAN; POPULATION SIZE; PRIORITY JOURNAL; SIGNAL NOISE RATIO; STARLING; STATE DEPENDENT LEARNING; STIMULUS RESPONSE; STURNUS VULGARIS; TASK PERFORMANCE;

ACOUSTIC STIMULATION; ACTION POTENTIALS; ANALYSIS OF VARIANCE; ANIMALS; ASSOCIATION LEARNING; AUDITORY CORTEX; AUDITORY PERCEPTION; INTERNEURONS; MALE; MODELS, NEUROLOGICAL; REACTION TIME; RECOGNITION (PSYCHOLOGY); STARLINGS; STATISTICS AS TOPIC;

EID: 84876804414     PISSN: 08966273     EISSN: 10974199     Source Type: Journal    
DOI: 10.1016/j.neuron.2013.02.023     Document Type: Article

References (55)

1. Abbott L.F., Dayan P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 1999, 11:91-101.
2. Alonso J.M., Usrey W.M., Reid R.C. Rules of connectivity between geniculate cells and simple cells in cat primary visual cortex. J. Neurosci. 2001, 21:4002-4015.
3. Averbeck B.B., Latham P.E., Pouget A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 2006, 7:358-366.
4. Bair W., Zohary E., Newsome W.T. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 2001, 21:1676-1697.
5. Barthó P., Hirase H., Monconduit L., Zugaro M., Harris K.D., Buzsáki G. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 2004, 92:600-608.
6. Bauer E.E., Coleman M.J., Roberts T.F., Roy A., Prather J.F., Mooney R. A synaptic basis for auditory-vocal integration in the songbird. J. Neurosci. 2008, 28:1509-1522.
7. Bejjanki V.R., Beck J.M., Lu Z.L., Pouget A. Perceptual learning as improved probabilistic inference in early sensory areas. Nat. Neurosci. 2011, 14:642-648.
8. Blake D.T., Strata F., Churchland A.K., Merzenich M.M. Neural correlates of instrumental learning in primary auditory cortex. Proc. Natl. Acad. Sci. USA 2002, 99:10114-10119.
9. Blake D.T., Heiser M.A., Caywood M., Merzenich M.M. Experience-dependent adult cortical plasticity requires cognitive association between sensation and reward. Neuron 2006, 52:371-381.
10. Cafaro J., Rieke F. Noise correlations improve response fidelity and stimulus encoding. Nature 2010, 468:964-967.
11. Cohen M.R., Kohn A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 2011, 14:811-819.
12. Cohen M.R., Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 2009, 12:1594-1600.
13. Cohen M.R., Newsome W.T. Context-dependent changes in functional circuitry in visual area MT. Neuron 2008, 60:162-173.
14. Constantinidis C., Goldman-Rakic P.S. Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J. Neurophysiol. 2002, 88:3487-3497.
15. Eens M. Understanding the complex song of the European starling: an integrated ethological approach. Adv. Stud. Behav. 1997, 26:355-434.
16. Fitzgerald J.D., Sincich L.C., Sharpee T.O. Minimal models of multidimensional computations. PLoS Comput. Biol. 2011, 7. e1001111.
17. Gentner T.Q., Hulse S.H. Perceptual classification based on the component structure of song in European starlings. J. Acoust. Soc. Am. 2000, 107:3369-3381.
18. Gentner T.Q., Margoliash D. Neuronal populations and single cells representing learned auditory objects. Nature 2003, 424:669-674.
19. Graf A.B., Kohn A., Jazayeri M., Movshon J.A. Decoding the activity of neuronal populations in macaque primary visual cortex. Nat. Neurosci. 2011, 14:239-245.
20. Gu Y., Liu S., Fetsch C.R., Yang Y., Fok S., Sunkara A., DeAngelis G.C., Angelaki D.E. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 2011, 71:750-761.
21. Gutnisky D.A., Dragoi V. Adaptive coding of visual information in neural populations. Nature 2008, 452:220-224.
22. Harris K.D., Henze D.A., Csicsvari J., Hirase H., Buzsáki G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 2000, 84:401-414.
23. Hartigan J.A., Hartigan P.M. The dip test of unimodality. Ann. Stat. 1985, 13:70-84.
24. Herrnstein R.J. Relative and absolute strength of response as a function of frequency of reinforcement. J. Exp. Anal. Behav. 1961, 4:267-272.
25. Hofer S.B., Ko H., Pichler B., Vogelstein J., Ros H., Zeng H., Lein E., Lesica N.A., Mrsic-Flogel T.D. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nat. Neurosci. 2011, 14:1045-1052.
26. Huber D., Petreanu L., Ghitani N., Ranade S., Hromádka T., Mainen Z., Svoboda K. Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice. Nature 2008, 451:61-64.
27. Jeanne J.M., Thompson J.V., Sharpee T.O., Gentner T.Q. Emergence of learned categorical representations within an auditory forebrain circuit. J. Neurosci. 2011, 31:2595-2606.
28. Knudsen D.P., Gentner T.Q. Active recognition enhances the representation of behaviorally relevant information in single auditory forebrain neurons. J. Neurophysiol. 2013, Published online January 9, 2013. 10.1152/jn.00461.2012.
29. Ko H., Hofer S.B., Pichler B., Buchanan K.A., Sjöström P.J., Mrsic-Flogel T.D. Functional specificity of local synaptic connections in neocortical networks. Nature 2011, 473:87-91.
30. Kohn A., Smith M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 2005, 25:3661-3673.
31. Lafferty, J., McCallum, A., and Pereira, F. (2001). Conditional random fields: probabilistic models for segmenting and labeling sequence data. C.E. Brodley and A.P. Danyluk, eds. Proceedings of the Eighteenth International Conference on Machine Learning (ICML '01), 282-289.
32. Law C.T., Gold J.I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 2008, 11:505-513.
33. Lee D., Port N.L., Kruse W., Georgopoulos A.P. Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex. J. Neurosci. 1998, 18:1161-1170.
34. Long J.S. Regression Models for Categorical and Limited Dependent Variables 1997, SAGE Publications, Thousand Oaks, CA.
35. Long M.A., Fee M.S. Using temperature to analyse temporal dynamics in the songbird motor pathway. Nature 2008, 456:189-194.
36. Meliza C.D., Margoliash D. Emergence of selectivity and tolerance in the avian auditory cortex. J. Neurosci. 2012, 32:15158-15168.
37. Meliza C.D., Chi Z., Margoliash D. Representations of conspecific song by starling secondary forebrain auditory neurons: toward a hierarchical framework. J. Neurophysiol. 2010, 103:1195-1208.
38. Michalski A., Gerstein G.L., Czarkowska J., Tarnecki R. Interactions between cat striate cortex neurons. Exp. Brain Res. 1983, 51:97-107.
39. Middleton J.W., Omar C., Doiron B., Simons D.J. Neural correlation is stimulus modulated by feedforward inhibitory circuitry. J. Neurosci. 2012, 32:506-518.
40. Mitchell J.F., Sundberg K.A., Reynolds J.H. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 2007, 55:131-141.
41. Mitchell J.F., Sundberg K.A., Reynolds J.H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 2009, 63:879-888.
42. Nottebohm F., Stokes T.M., Leonard C.M. Central control of song in the canary, Serinus canarius. J. Comp. Neurol. 1976, 165:457-486.
43. Oram M.W., Földiák P., Perrett D.I., Sengpiel F. The 'Ideal Homunculus': decoding neural population signals. Trends Neurosci. 1998, 21:259-265.
44. Reed A., Riley J., Carraway R., Carrasco A., Perez C., Jakkamsetti V., Kilgard M.P. Cortical map plasticity improves learning but is not necessary for improved performance. Neuron 2011, 70:121-131.
45. Renart A., de la Rocha J., Bartho P., Hollender L., Parga N., Reyes A., Harris K.D. The asynchronous state in cortical circuits. Science 2010, 327:587-590.
46. Romo R., Hernández A., Zainos A., Salinas E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 2003, 38:649-657.
47. Schmajuk N.A., Holland P.C. Occasion Setting: Associative Learning and Cognition in Animals 1998, American Psychological Association, Washington, DC.
48. Shadlen M.N., Newsome W.T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 1998, 18:3870-3896.
49. Shadlen M.N., Britten K.H., Newsome W.T., Movshon J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 1996, 16:1486-1510.
50. Tamura H., Kaneko H., Kawasaki K., Fujita I. Presumed inhibitory neurons in the macaque inferior temporal cortex: visual response properties and functional interactions with adjacent neurons. J. Neurophysiol. 2004, 91:2782-2796.
51. Thompson J.V., Gentner T.Q. Song recognition learning and stimulus-specific weakening of neural responses in the avian auditory forebrain. J. Neurophysiol. 2010, 103:1785-1797.
52. Thompson J.V., Jeanne J.M., Gentner T.Q. Local inhibition modulates learning-dependent song encoding in the songbird auditory cortex. J. Neurophysiol. 2013, 109:721-733.
53. Vigneswaran G., Kraskov A., Lemon R.N. Large identified pyramidal cells in macaque motor and premotor cortex exhibit "thin spikes": implications for cell type classification. J. Neurosci. 2011, 31:14235-14242.
54. Wilke S.D., Eurich C.W. Representational accuracy of stochastic neural populations. Neural Comput. 2002, 14:155-189.
55. Zohary E., Shadlen M.N., Newsome W.T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 1994, 370:140-143.