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Volumn 23, Issue 1-2, 2012, Pages 24-47

Learning stable, regularised latent models of neural population dynamics

Author keywords

Cortical microcircuitry; Motor control

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER INTERFACE; COMPUTER SIMULATION; ELECTRODE IMPLANT; MOTOR CORTEX; NERVE CELL NETWORK; NORMAL DISTRIBUTION; PHYSIOLOGY; POPULATION DYNAMICS; RHESUS MONKEY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 84861897852     PISSN: 0954898X     EISSN: 13616536     Source Type: Journal    
DOI: 10.3109/0954898X.2012.677095     Document Type: Article
Times cited : (32)

References (40)
  • 5
    • 13644261674 scopus 로고    scopus 로고
    • Optical imaging of neuronal populations during decision-making
    • Briggman KL, Abarbanel HDI, Kristan WB. 2005. Optical imaging of neuronal populations during decision-making. Science 307 (5711):896.
    • (2005) Science , vol.307 , Issue.5711 , pp. 896
    • Briggman, K.L.1    Hdi, A.2    Kristan, W.B.3
  • 6
    • 0032530363 scopus 로고    scopus 로고
    • A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells
    • Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA. 1998. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J Neurosci 18 (18):7411-7425. (Pubitemid 28425825)
    • (1998) Journal of Neuroscience , vol.18 , Issue.18 , pp. 7411-7425
    • Brown, E.N.1    Frank, L.M.2    Tang, D.3    Quirk, M.C.4    Wilson, M.A.5
  • 7
    • 2142765521 scopus 로고    scopus 로고
    • Multiple neural spike train data analysis: State-of-the-art and future challenges
    • DOI 10.1038/nn1228
    • Brown EN, Kass RE, Mitra PP. 2004. Multiple neural spike train data analysis: State-of-the-art and future challenges. Nat Neurosci 7 (5):456-461. (Pubitemid 38552378)
    • (2004) Nature Neuroscience , vol.7 , Issue.5 , pp. 456-461
    • Brown, E.N.1    Kass, R.E.2    Mitra, P.P.3
  • 8
    • 33645720928 scopus 로고    scopus 로고
    • Modeling sensorimotor learning with linear dynamical systems
    • DOI 10.1162/089976606775774651
    • Cheng S, Sabes PN. 2006. Modeling sensorimotor learning with linear dynamical systems. Neural Comput 18 (4):760-793. (Pubitemid 43543821)
    • (2006) Neural Computation , vol.18 , Issue.4 , pp. 760-793
    • Cheng, S.1    Sabes, P.N.2
  • 9
    • 0030285095 scopus 로고    scopus 로고
    • Realization of stable models with subspace methods
    • Chui NLC, Maciejowski JM. 1996. Realization of stable models with subspace methods. Automatica 32 (11):1587-1595.
    • (1996) Automatica , vol.32 , Issue.11 , pp. 1587-1595
    • Nlc, C.1    MacIejowski, J.M.2
  • 10
    • 33645575618 scopus 로고    scopus 로고
    • Neural variability in premotor cortex provides a signature of motor preparation
    • Churchland MM, Yu BM, Ryu S, Santhanam G, Shenoy KV. 2006. Neural variability in premotor cortex provides a signature of motor preparation. J Neurosci 26 (14):3697-3712.
    • (2006) J Neurosci , vol.26 , Issue.14 , pp. 3697-3712
    • Churchland, M.M.1    Yu, B.M.2    Ryu, S.3    Santhanam, G.4    Shenoy, K.V.5
  • 11
    • 37649008608 scopus 로고    scopus 로고
    • Techniques for extracting single-trial activity patterns from large-scale neural recordings
    • Churchland MM, Yu BM, Sahani M, Shenoy KV. 2007. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Curr Opin Neuro Biol 17 (5):609-618.
    • (2007) Curr Opin Neuro Biol , vol.17 , Issue.5 , pp. 609-618
    • Churchland, M.M.1    Yu, B.M.2    Sahani, M.3    Shenoy, K.V.4
  • 12
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the em algorithm
    • Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B 39 (1):1-38.
    • (1977) J R Stat Soc ser B , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 13
    • 0027681974 scopus 로고
    • Ml estimation of a stochastic linear system with the em algorithm and its application to speech recognition
    • Digalakis V, Rohlicek JR, Ostendorf M. 1993. Ml estimation of a stochastic linear system with the em algorithm and its application to speech recognition. IEEE Trans Speech Audio Process 1 (4):431-442.
    • (1993) IEEE Trans Speech Audio Process , vol.1 , Issue.4 , pp. 431-442
    • Digalakis, V.1    Rohlicek, J.R.2    Ostendorf, M.3
  • 16
  • 19
    • 39549113171 scopus 로고    scopus 로고
    • Imaging in vivo: Watching the brain in action
    • DOI 10.1038/nrn2338, PII NRN2338
    • Kerr JND, Denk W. 2008. Imaging in vivo: Watching the brain in action. Nat Rev Neurosci 9 (3):195-205. (Pubitemid 351278394)
    • (2008) Nature Reviews Neuroscience , vol.9 , Issue.3 , pp. 195-205
    • Kerr, J.N.D.1    Denk, W.2
  • 20
    • 58149333214 scopus 로고    scopus 로고
    • Advanced neurotechnologies for chronic neural interfaces: New horizons and clinical opportunities
    • Kipke DR, Shain W, Buzsaki G, Fetz E, Henderson JM, Hetke JF, Schalk G. 2008. Advanced neurotechnologies for chronic neural interfaces: New horizons and clinical opportunities. J Neurosci 28 (46):11830-11838.
    • (2008) J Neurosci , vol.28 , Issue.46 , pp. 11830-11838
    • Kipke, D.R.1    Shain, W.2    Buzsaki, G.3    Fetz, E.4    Henderson, J.M.5    Hetke, J.F.6    Schalk, G.7
  • 21
    • 35448977149 scopus 로고    scopus 로고
    • Common-input models for multiple neural spike-train data
    • Kulkarni JE, Paninski L. 2007. Common-input models for multiple neural spike-train data. Network 18 (4):375-407.
    • (2007) Network , vol.18 , Issue.4 , pp. 375-407
    • Kulkarni, J.E.1    Paninski, L.2
  • 23
    • 0042766804 scopus 로고    scopus 로고
    • Subspace identification with guaranteed stability using constrained optimization
    • Lacy SL, Bernstein DS. 2003. Subspace identification with guaranteed stability using constrained optimization. IEEE Trans Autom Contr 48 (7):1259-1263.
    • (2003) IEEE Trans Autom Contr , vol.48 , Issue.7 , pp. 1259-1263
    • Lacy, S.L.1    Bernstein, D.S.2
  • 30
    • 33646170322 scopus 로고    scopus 로고
    • Weak pairwise correlations imply strongly correlated network states in a neural population
    • Schneidman E, Berry MJ, Segev R, Bialek W. 2006. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440 (7087):1007-1012.
    • (2006) Nature , vol.440 , Issue.7087 , pp. 1007-1012
    • Schneidman, E.1    Berry, M.J.2    Segev, R.3    Bialek, W.4
  • 32
    • 79954552027 scopus 로고    scopus 로고
    • Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array
    • Simeral JD, Kim SP, Black MJ, Donoghue JP, Hochberg LR. 2011. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng 8 (2):025027, URL http://stacks.iop.org/1741-2552/8/i=2/a=025027
    • (2011) J Neural Eng , vol.8 , Issue.2 , pp. 025027
    • Simeral, J.D.1    Kim, S.P.2    Black, M.J.3    Donoghue, J.P.4    Hochberg, L.R.5
  • 33
    • 0038605051 scopus 로고    scopus 로고
    • Estimating a state-space model from point process observations
    • DOI 10.1162/089976603765202622
    • Smith AC, Brown EN. 2003. Estimating a state-space model from point process observations. Neural Comput 15 (5):965-991. (Pubitemid 37049798)
    • (2003) Neural Computation , vol.15 , Issue.5 , pp. 965-991
    • Smith, A.C.1    Brown, E.N.2
  • 34
    • 34247248843 scopus 로고    scopus 로고
    • A maximum-likelihood interpretation for slow feature analysis
    • Turner RE, Sahani M. 2007. A maximum-likelihood interpretation for slow feature analysis. Neural Comput 19 (4):1022-1038.
    • (2007) Neural Comput , vol.19 , Issue.4 , pp. 1022-1038
    • Turner, R.E.1    Sahani, M.2
  • 35
    • 0035440371 scopus 로고    scopus 로고
    • Identification of stable models in subspace identification by using regularization
    • DOI 10.1109/9.948469, PII S0018928601088225
    • Van Gestel T, Suykens JAK, Van Dooren P, De Moor B. 2001. Identification of stable models in subspace identification by using regularization. IEEE Trans Autom Contr 46 (9):1416-1420. (Pubitemid 32981645)
    • (2001) IEEE Transactions on Automatic Control , vol.46 , Issue.9 , pp. 1416-1420
    • Van Gestel, T.1    Suykens, J.A.K.2    Van Dooren, P.3    De Moor, B.4
  • 37
    • 33644876951 scopus 로고    scopus 로고
    • Bayesian population decoding of motor cortical activity using a Kalman filter
    • DOI 10.1162/089976606774841585
    • Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ. 2006. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput 18:80-118. (Pubitemid 43543852)
    • (2006) Neural Computation , vol.18 , Issue.1 , pp. 80-118
    • Wu, W.1    Gao, Y.2    Bienenstock, E.3    Donoghue, J.P.4    Black, M.J.5
  • 40
    • 67649610065 scopus 로고    scopus 로고
    • Gaussianprocess factor analysis for low-dimensional single-trial analysis of neural population activity
    • Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M. 2009. Gaussianprocess factor analysis for low-dimensional single-trial analysis of neural population activity. J Neurophysiol 102 (1):614-635.
    • (2009) J Neurophysiol , vol.102 , Issue.1 , pp. 614-635
    • Yu, B.M.1    Cunningham, J.P.2    Santhanam, G.3    Ryu, S.I.4    Shenoy, K.V.5    Sahani, M.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.