Dynamic population coding of category information in inferior temporal and prefrontal cortex

Journal of Neurophysiology

Volumn 100, Issue 3, 2008, Pages 1407-1419

  Meyers, Ethan M ^a,b   Freedman, David J ^c   Kreiman, Gabriel ^b,d   Miller, Earl K ^a,c   Poggio, Tomaso ^a,b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

^d BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ANIMAL EXPERIMENT; ARTICLE; BRAIN FUNCTION; CODING; DECISION MAKING; MACACA; NERVE STIMULATION; NONHUMAN; PREFRONTAL CORTEX; PRIORITY JOURNAL; TASK PERFORMANCE; TEMPORAL CORTEX; VISUAL INFORMATION;

ANIMALS; AUTOMATIC DATA PROCESSING; BEHAVIOR, ANIMAL; MACACA MULATTA; MODELS, NEUROLOGICAL; NEURONS; NONLINEAR DYNAMICS; PATTERN RECOGNITION, VISUAL; PHOTIC STIMULATION; PREFRONTAL CORTEX; REACTION TIME; TASK PERFORMANCE AND ANALYSIS; TEMPORAL LOBE; TIME FACTORS; VISUAL PATHWAYS;

EID: 57349146271     PISSN: 00223077     EISSN: 15221598     Source Type: Journal    
DOI: 10.1152/jn.90248.2008     Document Type: Article

References (46)

1. Abeles M. Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge, MA: Cambridge Univ. Press, 1991.
2. Aggelopoulos NC, Franco L, Rolls ET. Object perception in natural scenes: encoding by inferior temporal cortex simultaneously recorded neurons. J Neurophysiol 93: 1342-1357, 2005.
3. Anderson B, Sanderson MI, Sheinberg DL. Joint decoding of visual stimuli by IT neurons' spike counts is not improved by simultaneous recording. Exp Brain Res 176: 1-11, 2007.
4. Averbeck BB, Latham PE, Pouget A. Neural correlations, population coding and computation. Nat Rev Neurosci 7: 358-366, 2006.
5. Averbeck BB, Lee D. Effects of noise correlations on information encoding and decoding. J Neurophysiol 95: 3633-3644, 2006.
6. Baeg EH, Kim YB, Huh K, Mook-Jung I, Kim HT, Jung MW. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40: 177-188, 2003.
7. Dayan P, Abbott LF. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press, 2001.
8. deCharms RC, Zador A. Neural representation and the cortical code. Annu Rev Neurosci 23: 613-647, 2000.
9. Duda RO, Hart PE, Stork DG. Pattern Classification. New York: Wiley, 2001.
10. Field DJ. What is the goal of sensory coding. Neural Comput 6: 559-601, 1994.
11. Freedman DJ, Riesenhuber M, Poggio T, Miller EK. Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291: 312-316, 2001.
12. Freedman DJ, Riesenhuber M, Poggio T, Miller EK. Visual categorization and the primate prefrontal cortex: neurophysiology and behavior. J Neurophysiol 88: 929-941, 2002.
13. Freedman DJ, Riesenhuber M, Poggio T, Miller EK. A comparison of primate prefrontal and inferior temporal cortices during visual categorization. J Neurosci 23: 5235-5246, 2003.
14. Gochin PM, Colombo M, Dorfman GA, Gerstein GL, Gross CG. Neural ensemble coding in inferior temporal cortex. J Neurophysiol 71: 2325-2337, 1994.
15. Hahnloser RHR, Kozhevnikov AA, Fee MS. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419: 65-70, 2002.
16. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79: 2554-2558, 1982.
17. Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Fast readout of object identity from macaque inferior temporal cortex. Science 310: 863-866, 2005.
18. Laurent G. Olfactory network dynamics and the coding of multidimensional signals. Nat Rev Neurosci 3: 884-895, 2002.
19. McIlwain JT. Population coding: a historical sketch. In: Advances in Neural Population Coding, edited by Nicolelis MAL. Amsterdam: Elsevier, 2001, p. 3-7.
20. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24: 167-202, 2001.
21. Mogami T, Tanaka K. Reward association affects neuronal responses to visual stimuli in macaque TE and perirhinal cortices. J Neurosci 26: 6761-6770, 2006.
22. Nieder A, Freedman DJ, Miller EK. Representation of the quantity of visual items in the primate prefrontal cortex. Science 297: 1708-1711, 2002.
23. Nikolic D, Haeusler S, Singer W, Maass W. Temporal dynamics of information content carried by neurons in the primary visual cortex. In: Advances in Neural Information Processing Systems, edited by Scholkopf B, Platt J, Hoffman T. Cambridge, MA: MIT Press, 2007, p. 1041-1048.
24. Olshausen BA, Field DJ. Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Res 37: 3311-3325, 1997.
25. Paninski L. Estimation of entropy and mutual information. Neural Comput 15: 1191-1253, 2003.
26. Panzeri S, Pola G, Petersen RS. Coding of sensory signals by neuronal populations: the role of correlated activity. Neuroscientist 9: 175-180, 2003.
27. Perez-Orive J, Mazor O, Turner GC, Cassenaer S, Wilson RI, Laurent G. Oscillations and sparsening of odor representations in the mushroom body. Science 297: 359-365, 2002.
28. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. Invariant visual representation by single neurons in the human brain. Nature 435: 1102-1107, 2005.
29. Quiroga RQ, Snyder LH, Batista AP, Cui H, Andersen RA. Movement intention is better predicted than attention in the posterior parietal cortex. J Neurosci 26: 3615-3620, 2006.
30. Rainer G, Miller EK. Timecourse of object-related neural activity in the primate prefrontal cortex during a short-term memory task. Eur J Neurosci 15: 1244-1254, 2002.
31. Rainer G, Rao SC, Miller EK. Prospective coding for objects in primate prefrontal cortex. J Neurosci 19: 5493-5505, 1999.
32. Riesenhuber M, Poggio T. Models of object recognition. Nat Neurosci 3 (Suppl):1199 -1204, 2000.
33. Rolls ET, Tovee MJ. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J Neurophysiol 73: 713-726, 1995.
34. Rumelhart DE, McClelland JL, University of California San Diego. PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press, 1986.
35. Samengo I. Information loss in an optimal maximum likelihood decoding. Neural Comput 14: 771-779, 2002.
36. Serre T, Kouh M, Cadieu C, Knoblich U, Kreiman G, Poggio T. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex. CBCL Paper 259/AI Memo 2005-036, Massachusetts Institute of Technology, Cambridge, MA, 2005.
37. Seung HS, Sompolinsky H. Simple models for reading neuronal population codes. Proc Natl Acad Sci USA 90: 10749-10753, 1993.
38. Shima K, Isoda M, Mushiake H, Tanji J. Categorization of behavioral sequences in the prefrontal cortex. Nature 445: 315-318, 2007.
39. Shlens J, Kennel MB, Abarbanel HDI, Chichilnisky EJ. Estimating information rates with confidence intervals in neural spike trains. Neural Comput 19: 1683-1719, 2007.
40. Sigala N, Logothetis NK. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature 415: 318-320, 2002.
41. Stanley GB, Li FF, Dan Y. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. J Neurosci 19: 8036-8042, 1999.
42. Tanaka JW. Object categorization, expertise and neural plasticity. In: The New Cognitive Neurosciences, edited by Gazzaniga M. Cambridge, MA: MIT Press, 2004, p. 876-888.
43. Tomita H, Ohbayashi M, Nakahara K, Hasegawa I, Miyashita Y. Top-down signal from prefrontal cortex in executive control of memory retrieval. Nature 401: 699-703, 1999.
44. Ungerleider LG, Gaffan D, Pelak VS. Projections from inferior temporal cortex to prefrontal cortex via the uncinate fascicle in rhesus monkeys. Exp Brain Res 76: 473-484, 1989.
45. Zaksas D, Pasternak T. Directional signals in the prefrontal cortex and in area MT during a working memory for visual motion task. J Neurosci 26: 11726-11742, 2006.
46. Zemel RS, Dayan P, Pouget A. Probabilistic interpretation of population codes. Neural Comput 10: 403-430, 1998.