Characterizing learning by simultaneous analysis of continuous and binary measures of performance

Journal of Neurophysiology

Volumn 102, Issue 5, 2009, Pages 3060-3072

  Prerau, M J ^a,f   Smith, A C ^a   Eden, Uri T ^e,f   Kubota, Y ^b,c   Yanike, M ^d   Suzuki, W ^d   Graybiel, A M ^b,c   Brown, E N ^a,b  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d NEW YORK UNIVERSITY   (United States)

^e Boston University   (United States)

^f BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; COGNITION; CONTROLLED STUDY; LEARNING; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MAZE TEST; MONKEY; MOUSE; NONHUMAN; PRIORITY JOURNAL; TASK PERFORMANCE;

ALGORITHMS; ANIMALS; ASSOCIATION LEARNING; COGNITION; COMPUTER SIMULATION; HAPLORHINI; HUMANS; LEARNING; MODELS, NEUROLOGICAL; NONLINEAR DYNAMICS; PREDICTIVE VALUE OF TESTS; PROBABILITY; REACTION TIME;

EID: 70449433587     PISSN: 00223077     EISSN: 15221598     Source Type: Journal    
DOI: 10.1152/jn.91251.2008     Document Type: Article

References (42)

1. Barnes TD, Kubota Y, Hu D, Jin DZZ, Graybiel AM. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature 437: 1158-1161, 2005. (Pubitemid 41509353)
2. Chan KS, Ledolter J. Monte-Carlo Em estimation for time-series models involving counts. J Am Stat Assoc 90: 242-252, 1995.
3. Chater N, Oaksford M. The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford, UK: Oxford Univ. Press, 2008.
4. Dayan P, Yu A. Expected and unexpected uncertainty: Ach and NE in the neocortex. In: Advances in Neural Information Processing Sysytems, edited by Dietterich T, Becker S, Ghahramani Z. Cambridge, MA: MIT Press, 2002, p. 189-196.
5. Dejong P, Mackinnon MJ. Covariances for smoothed estimates in state-space models. Biometrika 75: 601-602, 1988.
6. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via Em algorithm. J Roy Stat Soc B Met 39: 1-38, 1977.
7. Dias R, Robbins TW, Roberts AC. Dissociable forms of inhibitory control within prefrontal cortex with an analog of the Wisconsin Card Sort Test: restriction to novel situations and independence from "on-line" processing. J Neurosci 17: 9285-9297, 1997.
8. Doucet A, De Freitas N, Gordon N. Sequential Monte Carlo Methods in Practice. New York: Springer, 2001.
9. Durbin J, Koopman SJ. Time Series Analysis by State Space Methods. Oxford, UKk: Oxford Univ. Press, 2001.
10. Dusek JA, Eichenbaum H. The hippocampus and memory for orderly stimulus relations. Proc Natl Acad Sci USA 94: 7109-7114, 1997.
11. Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Trans Biomed Eng 54: 419-428, 2007. (Pubitemid 46371228)
12. Fahrmeir L, Tutz G. Multivariate Statistical Modelling Based on Generalized Linear Models. New York: Springer, 2001.
13. Fox MT, Barense MD, Baxter MG. Perceptual attentional set-shifting is impaired in rats with neurotoxic lesions of posterior parietal cortex. J Neurosci 23: 676-681, 2003.
14. Gallistel CR. Learning and representation. In: Learning Theory and Behavior. Learning and Memory- A Comprehensive Reference, edited by Byrne J. Oxford: Elsevier, 2008, vol.1, p. 227-242.
15. Gallistel CR, Fairhurst S, Balsam P. The learning curve: implications of a quantitative analysis. Proc Natl Acad Sci USA 101: 13124-13131, 2004. (Pubitemid 39209629)
16. Gelman A. Bayesian Data Analysis. Boca Raton, FL: Chapman and Hall/CRC, 2004.
17. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442: 164-171, 2006. (Pubitemid 44086558)
18. Jog MS, Kubota Y, Connolly CI, Hillegaart V, Graybiel AM. Building neural representations of habits. Science 286: 1745-1749, 1999.
19. Kakade S, Dayan P. Acquisition and extinction in autoshaping. Psychol Rev 109: 533-544, 2002.
20. Kitagawa G, Gersch W. Smoothness Priors Analysis of Time Series. New York: Springer, 1996.
21. Law JR, Flanery MA, Wirth S, Yanike M, Smith AC, Frank LM, Suzuki WA, Brown EN, Stark CEL. Functional magnetic resonance imaging activity during the gradual acquisition and expression of paired-associate memory. J Neurosci 25: 5720-5729, 2005. (Pubitemid 40847884)
22. Liu AK, Belliveau JW, Dale AM. Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proc Natl Acad Sci USA 95: 8945-8950, 1998.
23. Mendel JM. Lessons in Estimation Theory for Signal Processing, Communications, and Control. Englewood Cliffs, NJ: Prentice Hall PTR, 1995.
24. Molins A, Stufflebeam SM, Brown EN, Hamalainen MS. Quantification of the benefit from integrating MEG and EEG data in minimum l(2)-norm estimation. Neuroimage 42: 1069-1077, 2008.
25. Prerau MJ, Smith AC, Eden UT, Yanike M, Suzuki WA, Brown EN. A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance. Biol Cybern 99: 1-14, 2008.
26. Rice JA. Mathematical Statistics and Data Analysis. Belmont, CA: Thomson/Brooks/Cole, 2007.
27. Roman FS, Simonetto I, Soumireumourat B. Learning and memory of odor-reward associatio-selective impairment following horizontal diagonal band lesions. Behav Neurosci 107: 72-81, 1993.
28. Rondi-Reig L, Libbey M, Eichenbaum H, Tonegawa S. CA1-specific N-methyl-d-aspartate receptor knockout mice are deficient in solving a nonspatial transverse patterning task. Proc Natl Acad Sci USA 98: 3543-3548, 2001.
29. Shumway R, Stoffer D. An approach to time series smoothing and forecasting using the EM algorithm. J Time Ser Anal 3: 253-264, 1982.
30. Siegel S, Castellan NJ. Nonparametric Statistics for the Behavioral Sciences. New York: McGraw-Hill, 1988.
31. Smith AC, Brown EN. Estimating a state-space model from point process observations. Neural Comput 15: 965-991, 2003. (Pubitemid 37049798)
32. Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki WA, Brown EN. Dynamic analysis of learning in behavioral experiments. J Neurosci 24: 447-461, 2004. (Pubitemid 38112376)
33. Smith AC, Stefani MR, Moghaddam B, Brown EN. Analysis and design of behavioral experiments to characterize population learning. J Neurophysiol 93: 1776-1792, 2005. (Pubitemid 40283755)
34. Smith AC, Wirth S, Suzuki WA, Brown EN. Bayesian analysis of interleaved learning and response bias in behavioral experiments. J Neurophysiol 97: 2516-2524, 2007.
35. Smith MA, Ghazizadeh A, Shadmehr R. Interacting adaptive processes with different time scales underlie short-term motor learning. Plos Biol 4: 1035-1043, 2006. (Pubitemid 43909461)
36. Srinivasan L, Eden UT, Mitter SK, Brown EN. General-purpose filter design for neural prosthetic devices. J Neurophysiol 98: 2456-2475, 2007.
37. Stefani MR, Groth K, Moghaddam B. Glutamate receptors in the rat medial prefrontal cortex regulate set-shifting ability. Behav Neurosci 117: 728-737, 2003.
38. Usher M, McClelland JL. The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev 108: 550-592, 2001. (Pubitemid 33604137)
39. Whishaw IQ, Tomie JA. Acquisition and retention by hippocampal rats of simple, conditional, and configural tasks using tactile and olfactory cues - implications for hippocampal function. Behav Neurosci 105: 787-797, 1991.
40. Williams ZM, Eskandar EN. Selective enhancement of associative learning by microstimulation of the anterior caudate. Nat Neurosci 9: 562-568, 2006.
41. Wirth S, Yanike M, Frank LM, Smith AC, Brown EN, Suzuki WA. Single neurons in the monkey hippocampus and learning of new associations. Science 300: 1578-1581, 2003.
42. Wise SP, Murray EA. Role of the hippocampal system in conditional motor learning: mapping antecedents to action. Hippocampus 9: 101-117, 1999