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Volumn 22, Issue 5, 2010, Pages 1231-1244

Nonconvergence in logistic and poisson models for neural spiking

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; COMPUTER PROGRAM; NERVE CELL; PHYSIOLOGY; POISSON DISTRIBUTION; STATISTICAL MODEL; TIME;

EID: 77953338745     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2010.03-09-982     Document Type: Article
Times cited : (16)

References (18)
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  • 2
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    • Convergence problems in logistic regression
    • M. Altman, J. Gill, & M. McDonald (Eds.). Hoboken, NJ: Wiley
    • Allison, P. (2004). Convergence problems in logistic regression. In M. Altman, J. Gill, & M. McDonald (Eds.), Numerical issues in statistical computing for the social scientists. Hoboken, NJ: Wiley.
    • (2004) Numerical issues in statistical computing for the social scientists
    • Allison, P.1
  • 5
    • 0023686914 scopus 로고
    • Maximum likelihood analysis of spike trains of interacting nerve cells
    • Brillinger, D. (1988).Maximum likelihood analysis of spike trains of interacting nerve cells. Biological Cybernetics, 59, 189-200.
    • (1988) Biological Cybernetics , vol.59 , pp. 189-200
    • Brillinger, D.1
  • 6
    • 67649383322 scopus 로고    scopus 로고
    • Principal fitted components for dimension reduction in regression
    • Cook, R.D., &Forzani, L. (2008) Principal fitted components for dimension reduction in regression. Statistical Science, 23, 485-501.
    • (2008) Statistical Science , vol.23 , pp. 485-501
    • Cook, R.D.1    Forzani, L.2
  • 7
    • 65249090828 scopus 로고    scopus 로고
    • Identifying functional connectivity in large-scale neural ensemble recordings: A multiscale data mining approach
    • Eldawlatly, S., Jin, R., & Oweiss, K.G. (2009). Identifying functional connectivity in large-scale neural ensemble recordings: A multiscale data mining approach. Neural Computation, 21, 450-477.
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  • 10
    • 0035432526 scopus 로고    scopus 로고
    • A spike-train probability model
    • Kass, R.E., & Ventura, V.E. (2001). A spike-train probability model. Neural Computation, 13, 1713-1720.
    • (2001) Neural Computation , vol.13 , pp. 1713-1720
    • Kass, R.E.1    Ventura, V.E.2
  • 12
    • 9744274025 scopus 로고    scopus 로고
    • Maximum likelihood estimation of cascade point-process neural encoding models
    • Paninski, L. (2004).Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems, 15, 243-262.
    • (2004) Network: Computation in Neural Systems , vol.15 , pp. 243-262
    • Paninski, L.1
  • 13
    • 0001431651 scopus 로고
    • A note on A Albert and J. A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models
    • Santner, T.J., & Duffy, D.E. (1986). A note on A. Albert and J.A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models. Biometrika, 73, 755-758.
    • (1986) Biometrika , vol.73 , pp. 755-758
    • Santner, T.J.1    Duffy, D.E.2
  • 14
    • 0000950512 scopus 로고
    • On the existence of maximum likelihood estimators for the binomial response models
    • Silvapulle, M. (1981). On the existence of maximum likelihood estimators for the binomial response models. J. Royal Statistical Society, B, 43, 310-313.
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  • 16
    • 12544253489 scopus 로고    scopus 로고
    • A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects
    • Truccolo, W., Eden, U., Fellows, M., Donoghue, J., & Brown, E. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiology, 93, 1074-1089.
    • (2005) J. Neurophysiology , vol.93 , pp. 1074-1089
    • Truccolo, W.1    Eden, U.2    Fellows, M.3    Donoghue, J.4    Brown, E.5
  • 17
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.