Learning enhances the relative impact of top-down processing in the visual cortex

Nature Neuroscience

Volumn 18, Issue 8, 2015, Pages 1116-1122

  Makino, Hiroshi ^a   Komiyama, Takaki ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL CELL; ANIMAL EXPERIMENT; ARTICLE; AVOIDANCE BEHAVIOR; BRAIN REGION; CONTROLLED STUDY; FEMALE; IN VITRO STUDY; IN VIVO STUDY; LEARNING; MALE; MOUSE; NERVE CELL PLASTICITY; NEUROMODULATION; NONHUMAN; PHOTOACTIVATION; PRIORITY JOURNAL; RESPONSE TIME; RETROSPLENIAL CORTEX; STATE DEPENDENT LEARNING; STRIATE CORTEX; VISUAL STIMULATION; ANIMAL; ANIMAL BEHAVIOR; CYTOLOGY; INTERNEURON; LASER SCANNING CYTOMETRY; METABOLISM; NERVE CELL; PHYSIOLOGY; VISION; VISUAL CORTEX; VISUAL SYSTEM;

4 AMINOBUTYRIC ACID RECEPTOR; CALCIUM; SOMATOSTATIN;

ANIMALS; ASSOCIATION LEARNING; BEHAVIOR, ANIMAL; CALCIUM; FEMALE; GABAERGIC NEURONS; INTERNEURONS; LASER SCANNING CYTOMETRY; MALE; MICE; NEURONS; SOMATOSTATIN; VISUAL CORTEX; VISUAL PATHWAYS; VISUAL PERCEPTION;

EID: 84938353162     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.4061     Document Type: Article

References (57)

1. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193-222 (1995).
2. Hupé, J.M. et al. Cortical feedback improves discrimination between fgure and background by V1, V2 and V3 neurons. Nature 394, 784-787 (1998).
3. Engel, A.K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704-716 (2001).
4. Krupa, D.J., Wiest, M.C., Shuler, M.G., Laubach, M. & Nicolelis, M.A. Layer-specifc somatosensory cortical activation during active tactile discrimination. Science 304, 1989-1992 (2004).
5. Nienborg, H. & Cumming, B.G. Decision-related activity in sensory neurons refects more than a neuron's causal effect. Nature 459, 89-92 (2009).
6. Gilbert, C.D. & Li, W. Top-down infuences on visual processing. Nat. Rev. Neurosci. 14, 350-363 (2013).
7. Harris, K.D. & Mrsic-Flogel, T.D. Cortical connectivity and sensory coding. Nature 503, 51-58 (2013).
8. Rao, R.P. & Ballard, D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-feld effects. Nat. Neurosci. 2, 79-87 (1999).
9. Hinton, G.E. Learning multiple layers of representation. Trends Cogn. Sci. 11, 428-434 (2007).
10. Friston, K. The free-energy principle: a unifed brain theory? Nat. Rev. Neurosci. 11, 127-138 (2010).
11. Olshausen, B.A. & Field, D.J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481-487 (2004).
12. Bastos, A.M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695-711 (2012).
13. Gdalyahu, A. et al. Associative fear learning enhances sparse network coding in primary sensory cortex. Neuron 75, 121-132 (2012).
14. Mignard, M. & Malpeli, J.G. Paths of information fow through visual cortex. Science 251, 1249-1251 (1991).
15. Felleman, D.J. & Van Essen, D.C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1-47 (1991).
16. Petreanu, L., Mao, T., Sternson, S.M. & Svoboda, K. The subcellular organization of neocortical excitatory connections. Nature 457, 1142-1145 (2009).
17. Cauller, L.J. & Connors, B.W. Synaptic physiology of horizontal afferents to layer I in slices of rat SI neocortex. J. Neurosci. 14, 751-762 (1994).
18. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660-665 (2014).
19. Vann, S.D., Aggleton, J.P. & Maguire, E.A. What does the retrosplenial cortex do? Nat. Rev. Neurosci. 10, 792-802 (2009).
20. Lukoyanov, N.V. & Lukoyanova, E.A. Retrosplenial cortex lesions impair acquisition of active avoidance while sparing fear-based emotional memory. Behav. Brain Res. 173, 229-236 (2006).
21. Gentet, L.J. et al. Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex. Nat. Neurosci. 15, 607-612 (2012).
22. Palmer, L., Murayama, M. & Larkum, M. Inhibitory regulation of dendritic activity in vivo. Front. Neural Circuits 6, 26 (2012).
23. Dombeck, D.A., Harvey, C.D., Tian, L., Looger, L.L. & Tank, D.W. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat. Neurosci. 13, 1433-1440 (2010).
24. Glickfeld, L.L., Histed, M.H. & Maunsell, J.H. Mouse primary visual cortex is used to detect both orientation and contrast changes. J. Neurosci. 33, 19416-19422 (2013).
25. Chen, T.W. et al. Ultrasensitive fuorescent proteins for imaging neuronal activity. Nature 499, 295-300 (2013).
26. Taniguchi, H. et al. A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron 71, 995-1013 (2011).
27. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133-140 (2010).
28. Peters, A.J., Chen, S.X. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263-267 (2014).
29. Janssen, P. & Shadlen, M.N. A representation of the hazard rate of elapsed time in macaque area LIP. Nat. Neurosci. 8, 234-241 (2005).
30. Shuler, M.G. & Bear, M.F. Reward timing in the primary visual cortex. Science 311, 1606-1609 (2006).
31. Niell, C.M. & Stryker, M.P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472-479 (2010).
32. Cauller, L. Layer I of primary sensory neocortex: where top-down converges upon bottom-up. Behav. Brain Res. 71, 163-170 (1995).
33. Kawaguchi, Y. & Kubota, Y. GABAergic cell subtypes and their synaptic connections in rat frontal cortex. Cereb. Cortex 7, 476-486 (1997).
34. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793-807 (2004).
35. Brown, S.P. & Hestrin, S. Cell-type identity: a key to unlocking the function of neocortical circuits. Curr. Opin. Neurobiol. 19, 415-421 (2009).
36. Kepecs, A. & Fishell, G. Interneuron cell types are ft to function. Nature 505, 318-326 (2014).
37. Lovett-Barron, M. et al. Dendritic inhibition in the hippocampus supports fear learning. Science 343, 857-863 (2014).
38. Ma, W.P. et al. Visual representations by cortical somatostatin inhibitory neurons: selective but with weak and delayed responses. J. Neurosci. 30, 14371-14379 (2010).
39. Kerlin, A.M., Andermann, M.L., Berezovskii, V.K. & Reid, R.C. Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67, 858-871 (2010).
40. Yizhar, O. et al. Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature 477, 171-178 (2011).
41. Callaway, E.M. Feedforward, feedback and inhibitory connections in primate visual cortex. Neural Netw. 17, 625-632 (2004).
42. Vélez-Fort, M. et al. The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing. Neuron 83, 1431-1443 (2014).
43. Olsen, S.R., Bortone, D.S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47-52 (2012).
44. Bortone, D.S., Olsen, S.R. & Scanziani, M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex. Neuron 82, 474-485 (2014).
45. Cottam, J.C., Smith, S.L. & Hausser, M. Target-specifc effects of somatostatin-expressing interneurons on neocortical visual processing. J. Neurosci. 33, 19567-19578 (2013).
46. Pfeffer, C.K., Xue, M., He, M., Huang, Z.J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068-1076 (2013).
47. Xu, H., Jeong, H.Y., Tremblay, R. & Rudy, B. Neocortical somatostatin-expressing GABAergic interneurons disinhibit the thalamorecipient layer 4. Neuron 77, 155-167 (2013).
48. Letzkus, J.J. et al. A disinhibitory microcircuit for associative fear learning in the auditory cortex. Nature 480, 331-335 (2011).
49. Deco, G. & Rolls, E.T. A neurodynamical cortical model of visual attention and invariant object recognition. Vision Res. 44, 621-642 (2004).
50. Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141-151 (2013).
51. Brainard, D.H. The Psychophysics Toolbox. Spat. Vis. 10, 433-436 (1997).
52. Pologruto, T.A., Sabatini, B.L. & Svoboda, K. ScanImage: fexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003).
53. Thévenaz, P., Ruttimann, U.E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27-41 (1998).
54. Dombeck, D.A., Khabbaz, A.N., Collman, F., Adelman, T.L. & Tank, D.W. Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56, 43-57 (2007).
55. Ringach, D.L., Hawken, M.J. & Shapley, R. Dynamics of orientation tuning in macaque primary visual cortex. Nature 387, 281-284 (1997).
56. Paxinos, G. & Franklin, K.B.J. Paxinos and Franklin's the Mouse Brain in Stereotaxic Coordinates (Boston: Elsevier/Academic Press, Amsterdam, 2013).
57. Zhao, S. et al. Cell type-specifc channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function. Nat. Methods 8, 745-752 (2011).