The receptive field is dead. Long live the receptive field?

Current Opinion in Neurobiology

Volumn 25, Issue , 2014, Pages 9-12

  Fairhall, Adrienne ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER; CONNECTOME; MACHINE LEARNING; MATHEMATICAL COMPUTING; NONHUMAN; PRIORITY JOURNAL; RECEPTIVE FIELD; REVIEW; SENSORY STIMULATION; STIMULUS RESPONSE; TASK PERFORMANCE; ANIMAL; ARTIFICIAL NEURAL NETWORK; HUMAN; NERVE CELL NETWORK; NERVOUS SYSTEM FUNCTION; PHYSIOLOGY; SENSATION;

ANIMALS; HUMANS; NERVE NET; NERVOUS SYSTEM PHYSIOLOGICAL PHENOMENA; NEURAL NETWORKS (COMPUTER); SENSATION;

EID: 84897574981     PISSN: 09594388     EISSN: 18736882     Source Type: Journal    
DOI: 10.1016/j.conb.2014.02.001     Document Type: Review

References (48)

1. Poggio T., Mutch J., Leibo J., Rosasco L., Tacchetti A. The Computational Magic of the Ventral Stream: Sketch of a Theory (and why some deep architectures work). MIT Technical Report, MIT-CSAIL-TR-2012-35 2012, http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2012-035.pdf.
2. Maravall M., Diamond M. Algorithms of whisker-mediated touch perception. Curr Opin Neurobiol 2014, 25:176-186.
3. Shamma S., Fritz J. Adaptive auditory computations. Curr Opin Neurobiol 2014, 25:164-168.
4. Secundo L., Snitz K., Sobel N. The perceptual logic of smell. Curr Opin Neurobiol 2014, 25:107-115.
5. Miura K., Mainen Z.F., Uchida N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 2012, 74:1087-1098.
6. Sharpee T.O. Computational identification of receptive fields. Annu Rev Neurosci 2013, 36:103-120.
7. Pillow J.W., Shlens J., Paninski L., Sher A., Litke A.M., Chichilnisky E.J., Simoncelli E.P. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 2008, 454:995-999.
8. Sharpee T.O., Calhoun A.J., Chalasani S.H. Information theory of adaptation in neurons, behavior, and mood. Curr Opin Neurobiol 2014, 25:47-53.
9. Olshausen B.A., Field D.J. Sparse coding of sensory inputs. Curr Opin Neurobiol 2004, 14:481-487.
10. Cox D.D. Do we understand high-level vision?. Curr Opin Neurobiol 2014, 25:187-193.
11. DiCarlo J.J., Zoccolan D., Rust N.C. How does the brain solve visual object recognition?. Neuron 2012, 73(February):415-434.
12. Adibi M., McDonald J.S., Clifford C.W., Arabzadeh E. Adaptation improves neural coding efficiency despite increasing correlations in variability. J Neurosci 2013, 33:2108-2120.
13. Wark B., Lundstrom B.N., Fairhall A. Sensory adaptation. Curr Opin Neurobiol 2007, 17:423-429.
14. Bengio Y. Learning deep architectures for AI. Found Trends Mach Learn 2009, 2.
15. Rieke F., Rudd M.E. The challenges natural images pose for visual adaptation. Neuron 2009, 64:605-616.
16. Kastner D.K., Baccus S.A. Insights from the retina into the diverse and general computations of adaptation, detection, and prediction. Curr Opin Neurobiol 2014, 25:63-69.
17. Seilheimer R.L., Rosenberg A., Angelaki D.E. Models and processes of multisensory cue combination. Curr Opin Neurobiol 2014, 25:38-46.
18. Roth E., Sponberg S., Cowan N.J. A comparative approach to closed-loop computation. Curr Opin Neurobiol 2014, 25:54-62.
19. Cohen N., Sanders T. Nematode locomotion: dissecting the neuronal-environmental loop. Curr Opin Neurobiol 2014, 25:99-106.
20. Brunel N., Hakim V., Richardson M.J.E. Single neuron dynamics and computation. Curr Opin Neurobiol 2014, 25:149-155.
21. Hong S.S.H., Agüera B., Arcas A.L., Fairhall Single neuron computation: from dynamical system to feature detector. Neural Comput 2007, 19:3133-3172.
22. Mease R.A., Famulare M., Gjorgjieva J., Moody W.J., Fairhall A.L. Emergence of adaptive computation by single neurons in the developing cortex. J Neurosci 2013, 33:12154-12170.
23. Eliasmith C., Trujillo O. The use and abuse of large-scale brain models. Curr Opin Neurobiol 2014, 25(April):1-6.
24. Wolf F., Engelken R., Puelma-Touzel M., Flórez Weidinger J.D., Neef A. Dynamical models of cortical circuits. Curr Opin Neurobiol 2014, 25.
25. Schaffer E.S., Ostojic S., Abbott L.F. A complex-valued firing-rate model that approximates the dynamics of spiking networks. PLoS Comput Biol 2013, 9:e1003301.
26. Gjorgjieva J., Famulare M., Mease R., Moody W.J., Fairhall A.L. Implications of Single-Neuron Gain Control for Information Transmission in Networks. Computational and Systems Neuroscience (COSYNE) 2011, Available from Nature Precedings, http://precedings.nature.com/documents/5860/version/1.
27. Harris K., Mrsic-Flogel T.D. Cortical connectivity and sensory coding. Nature 2013, 503:51-58.
28. Ching S., Brown E.N. Modeling the dynamical effects of anesthesia on brain circuits. Curr Opin Neurobiol 2014, 25:116-122.
29. Bargmann C.I., Marder E. From the connectome to brain function. Nat Methods 2013, 10:483-490.
30. Nakahara H. Multiplexing signals in reinforcement learning with internal models and dopamine. Curr Opin Neurobiol 2014, 25:123-129.
31. Fee M. The role of efference copy in striatal reinforcement learning. Curr Opin Neurobiol 2014, 25:194-200.
32. Hu Y., Trousdale J., Josić K., Shea-Brown E. Motif statistics and spike correlations in neuronal networks. J Stat Mech 2013, 10.1088/1742-5468/2013/03/P03012.
33. Takemura S.Y., Bharioke A., Lu Z., Nern A., Vitaladevuni S., Rivlin P.K., Katz W.T., Olbris D.J., Plaza S.M., Winston P., Zhao T., Horne J.A., Fetter R.D., Takemura S., Blazek K., Chang L.A., Ogundeyi O., Saunders M.A., Shapiro V., Sigmund C., Rubin G.M., Scheffer L.K., Meinertzhagen I.A., Chklovskii D.B. A visual motion detection circuit suggested by Drosophila connectomics. Nature 2013, 500:175-181.
34. Helmstaedter M., Briggman K.L., Turaga S.C., Jain V., Seung H.S., Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 2013, 500:168-174.
35. Elyada Y.M., Haag J., Borst A. Different receptive fields in axons and dendrites underlie robust coding in motion-sensitive neurons. Nat Neurosci 2009, 12:327-332.
36. Jeremiah D.W., Rao R.P.N. Brain-computer interfaces: a powerful tool for scientific inquiry. Curr Opin Neurobiol 2014, 25:70-75.
37. Velliste M., Perel S., Spalding M.C., Whitford A.S., Schwartz A.B. Cortical control of a prosthetic arm for self-feeding. Nature 2008, 453:1098-1101.
38. Carmena J.M. Advances in neuroprosthetic learning and control. PLoS Biol 2013, 11:e1001561. 10.1371/journal.pbio.1001561.
39. Fetz E.E. Operant conditioning of cortical unit activity. Science 1969, 163:955-957.
40. Legenstein R., Chase S.M., Schwartz A.B., Maass W. A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J Neurosci 2010, 30(June):8400-8410.
41. Shenoy K.M., Sahani M., Churchland Cortical control of arm movements: a dynamical systems perspective. Annu Rev Neurosci 2013, 36:337-359.
42. Roy J.E., Riesenhuber M., Poggio T., Miller E.K. Prefrontal cortex activity during flexible categorization. J Neurosci 2010, 30:8519-8528.
43. Mante V., Sussillo D., Shenoy K.V., Newsome W.T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 2013, 503:78-84.
44. Kaufman M.T., Raposo D., Churchland A.K. Dynamics of decision and action in rat posterior parietal cortex. Soc Neurosci 2013, 668.04.
45. Sussillo D. Neural circuits as computational dynamical systems. Curr Opin Neurobiol 2014, 25:156-163.
46. Hedrick K.R., Cox S.J. Structure-preserving model reduction of passive and quasi-active neurons. J Comput Neurosci 2013, 34:1-26.
47. Keller G.B., Bonhoeffer T., Hübener M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 2012, 74:809-815.
48. Mnih V., Kavukcuoglu K., Silver D., Graves A., Antonoglou I., Wierstra D., Riedmiller M. Playing Atari with Deep Reinforcement Learning. arxiv:1312.5602.