Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex

Nature Neuroscience

Volumn 19, Issue 2, 2016, Pages 299-307

  Roth, Morgane M ^a   Dahmen, Johannes C ^b,c   Muir, Dylan R ^a   Imhof, Fabia ^a   Martini, Francisco J ^a   Hofer, Sonja B ^a,b  

^a UNIVERSITY OF BASEL   (Switzerland)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; CONTROLLED STUDY; GENICULATE NUCLEUS; INFORMATION; MOUSE; NERVE FIBER; NONHUMAN; PRIORITY JOURNAL; RUNNING; STRIATE CORTEX; THALAMOCORTICAL TRACT; THALAMUS; THALAMUS NUCLEUS; THALAMUS POSTERIOR NUCLEUS; VISUAL CORTEX; ALGORITHM; ANIMAL; AXON; C57BL MOUSE; EFFERENT NERVE; ELECTROPHYSIOLOGY; FEEDBACK SYSTEM; FEMALE; GENICULATE BODY; MALE; MOTOR CORTEX; MOVEMENT PERCEPTION; NERVE TRACT; NEUROIMAGING; PHOTOSTIMULATION; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE; SENSATION; SENSORY NERVE; VISUAL SYSTEM;

AFFERENT PATHWAYS; ALGORITHMS; ANIMALS; AXONS; EFFERENT PATHWAYS; ELECTROPHYSIOLOGICAL PROCESSES; FEEDBACK, PHYSIOLOGICAL; FEMALE; GENICULATE BODIES; MALE; MICE; MICE, INBRED C57BL; MOTION PERCEPTION; MOTOR CORTEX; NEURAL PATHWAYS; NEUROIMAGING; PHOTIC STIMULATION; PSYCHOMOTOR PERFORMANCE; SENSATION; THALAMIC NUCLEI; VISUAL CORTEX; VISUAL PATHWAYS;

EID: 84956703469     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.4197     Document Type: Article

References (62)

1. Gilbert, C.D. & Wiesel, T.N. The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat. Vision Res. 30, 1689-1701 (1990).
2. Roelfsema, P.R., Lamme, V.A.F. & Spekreijse, H. Object-based attention in the primary visual cortex of the macaque monkey. Nature 395, 376-381 (1998).
3. Gilbert, C.D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350-363 (2013).
4. Niell, C.M. & Stryker, M.P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472-479 (2010).
5. Keller, G.B., Bonhoeffer, T. & Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74, 809-815 (2012).
6. Saleem, A.B., Ayaz, A., Jeffery, K.J., Harris, K.D. & Carandini, M. Integration of visual motion and locomotion in mouse visual cortex. Nat. Neurosci. 16, 1864-1869 (2013).
7. Mumford, D. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 66, 241-251 (1992).
8. Rao, R.P.N. & Ballard, D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79-87 (1999).
9. Makino, H. & Komiyama, T. Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18, 1116-1122 (2015).
10. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660-665 (2014).
11. Lee, A.M. et al. Identification of a brainstem circuit regulating visual cortical state in parallel with locomotion. Neuron 83, 455-466 (2014).
12. Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139-1152 (2014).
13. McAlonan, K., Cavanaugh, J. & Wurtz, R.H. Guarding the gateway to cortex with attention in visual thalamus. Nature 456, 391-394 (2008).
14. Erisken, S. et al. Effects of locomotion extend throughout the mouse early visual system. Curr. Biol. 24, 2899-2907 (2014).
15. Williamson, R.S., Hancock, K.E., Shinn-Cunningham, B.G. & Polley, D.B. Locomotion and task demands differentially modulate thalamic audiovisual processing during active search. Curr. Biol. 25, 1885-1891 (2015).
16. Sherman, S.M. & Guillery, R.W. The role of the thalamus in the flow of information to the cortex. Phil. Trans. R. Soc. Lond. B 357, 1695-1708 (2002).
17. Shipp, S. The functional logic of cortico-pulvinar connections. Phil. Trans. R. Soc. Lond. B 358, 1605-1624 (2003).
18. Grieve, K.L., Acuña, C. & Cudeiro, J. The primate pulvinar nuclei: vision and action. Trends Neurosci. 23, 35-39 (2000).
19. Saalmann, Y.B. & Kastner, S. Cognitive and perceptual functions of the visual thalamus. Neuron 71, 209-223 (2011).
20. Kaas, J.H. & Lyon, D.C. Pulvinar contributions to the dorsal and ventral streams of visual processing in primates. Brain Res. Rev. 55, 285-296 (2007).
21. Raczkowski, D. & Rosenquist, A.C. Connections of the multiple visual cortical areas with the lateral posterior-pulvinar complex and adjacent thalamic nuclei in the cat. J. Neurosci. 3, 1912-1942 (1983).
22. Bender, D.B. Receptive-field properties of neurons in the macaque inferior pulvinar. J. Neurophysiol. 48, 1-17 (1982).
23. Saalmann, Y.B., Pinsk, M.A., Wang, L., Li, X. & Kastner, S. The pulvinar regulates information transmission between cortical areas based on attention demands. Science 337, 753-756 (2012).
24. Purushothaman, G., Marion, R., Li, K. & Casagrande, V.A. Gating and control of primary visual cortex by pulvinar. Nat. Neurosci. 15, 905-912 (2012).
25. Baldwin, M.K.L., Wong, P., Reed, J.L. & Kaas, J.H. Superior colliculus connections with visual thalamus in gray squirrels (Sciurus carolinensis): evidence for four subdivisions within the pulvinar complex. J. Comp. Neurol. 519, 1071-1094 (2011).
26. Wurtz, R.H., McAlonan, K., Cavanaugh, J. & Berman, R.A. Thalamic pathways for active vision. Trends Cogn. Sci. 15, 177-184 (2011).
27. Kamishina, H. et al. Cortical connections of the rat lateral posterior thalamic nucleus. Brain Res. 1264, 39-56 (2009).
28. Gale, S.D. & Murphy, G.J. Distinct representation and distribution of visual information by specific cell types in mouse superficial superior colliculus. J. Neurosci. 34, 13458-13471 (2014).
29. Rubio-Garrido, P., Pérez-de-Manzo, F., Porrero, C., Galazo, M.J. & Clascá, F. Thalamic input to distal apical dendrites in neocortical layer 1 is massive and highly convergent. Cereb. Cortex 19, 2380-2395 (2009).
30. Cruz-Martín, A. et al. A dedicated circuit links direction-selective retinal ganglion cells to the primary visual cortex. Nature 507, 358-361 (2014).
31. Bickford, M.E., Zhou, N., Krahe, T.E., Govindaiah, G. & Guido, W. Retinal and tectal "driver-like" inputs converge in the shell of the mouse dorsal lateral geniculate nucleus. J. Neurosci. 35, 10523-10534 (2015).
32. Tohmi, M., Meguro, R., Tsukano, H., Hishida, R. & Shibuki, K. The extrageniculate visual pathway generates distinct response properties in the higher visual areas of mice. Curr. Biol. 24, 587-597 (2014).
33. Piscopo, D.M., El-Danaf, R.N., Huberman, A.D. & Niell, C.M. Diverse visual features encoded in mouse lateral geniculate nucleus. J. Neurosci. 33, 4642-4656 (2013).
34. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295-300 (2013).
35. De Paola, V. et al. Cell type-specific structural plasticity of axonal branches and boutons in the adult neocortex. Neuron 49, 861-875 (2006).
36. Petreanu, L. et al. Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature 489, 299-303 (2012).
37. Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473-478 (2012).
38. Grubb, M.S. & Thompson, I.D. Quantitative characterization of visual response properties in the mouse dorsal lateral geniculate nucleus. J. Neurophysiol. 90, 3594-3607 (2003).
39. Marshel, J.H., Kaye, A.P., Nauhaus, I. & Callaway, E.M. Anterior-posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron 76, 713-720 (2012).
40. Zhao, X., Chen, H., Liu, X. & Cang, J. Orientation-selective responses in the mouse lateral geniculate nucleus. J. Neurosci. 33, 12751-12763 (2013).
41. Scholl, B., Tan, A.Y.Y., Corey, J. & Priebe, N.J. Emergence of orientation selectivity in the Mammalian visual pathway. J. Neurosci. 33, 10616-10624 (2013).
42. Liang, F. et al. Sensory cortical control of a visually induced arrest behavior via corticotectal projections. Neuron 86, 755-767 (2015).
43. Sillito, A.M., Cudeiro, J. & Jones, H.E. Always returning: feedback and sensory processing in visual cortex and thalamus. Trends Neurosci. 29, 307-316 (2006).
44. McCormick, D.A. Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity. Prog. Neurobiol. 39, 337-388 (1992).
45. Kropff, E., Carmichael, J.E., Moser, M.-B. & Moser, E.I. Speed cells in the medial entorhinal cortex. Nature 523, 419-424 (2015).
46. Friston, K. A theory of cortical responses. Phil. Trans. R. Soc. Lond. B 360, 815-836 (2005).
47. Robinson, D.L. & Petersen, S.E. The pulvinar and visual salience. Trends Neurosci. 15, 127-132 (1992).
48. Guillery, R.W. & Sherman, S.M. Branched thalamic afferents: what are the messages that they relay to the cortex? Brain Res. Rev. 66, 205-219 (2011).
49. Harris, K.D. & Mrsic-Flogel, T.D. Cortical connectivity and sensory coding. Nature 503, 51-58 (2013).
50. Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141-151 (2013).
51. Akerboom, J. et al. Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819-13840 (2012).
52. Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433-436 (1997).
53. Pologruto, T.A., Sabatini, B.L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003).
54. Leinweber, M. et al. Two-photon calcium imaging in mice navigating a virtual reality environment. J. Vis. Exp. 84, e50885 (2014).
55. Poort, J. et al. Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86, 1478-1490 (2015).
56. Guizar-Sicairos, M., Thurman, S.T. & Fienup, J.R. Efficient subpixel image registration algorithms. Opt. Lett. 33, 156-158 (2008).
57. Smith, S.L. & Häusser, M. Parallel processing of visual space by neighboring neurons in mouse visual cortex. Nat. Neurosci. 13, 1144-1149 (2010).
58. Mukamel, E.A., Nimmerjahn, A. & Schnitzer, M.J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747-760 (2009).
59. Dyer, E.L., Studer, C., Robinson, J.T. & Baraniuk, R.G. A robust and efficient method to recover neural events from noisy and corrupted data. Int. IEEE/EMBS Conf. Neural Eng. NER 593-596 (2013).
60. Gandy, A. & Hahn, G. MMCTest - a safe algorithm for implementing multiple Monte Carlo tests. Scand. J. Stat. 41, 1083-1101 (2014).
61. 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. 84, 401-414 (2000).
62. Paxinos, G. & Franklin, K.B.J. The Mouse Brain in Stereotaxic Coordinates (Academic, 2001).