On nonlinearity in neural encoding models applied to the primary visual cortex

References

• Ahrens MB, Paninski L, Sahani M. Inferring input nonlinearities in neural encoding models. Network: Computation in Neural Systems 2008; 19: 35–67
   PubMed Web of Science ®Google Scholar
• Averbeck BB, Sohn JW, Lee D. Activity in prefrontal cortex during dynamic selection of action sequences. Nature Neuroscience 2006; 9: 276–282
   PubMed Web of Science ®Google Scholar
• Bezdudnaya T, Cano M, Bereshpolova Y, Stoelzel CR, Alonso JM, Swadlow HA. Thalamic burst mode and inattention in the awake LGND. Neuron 2006; 49: 421–432
   PubMed Web of Science ®Google Scholar
• Brenner N, Bialek W, de Ruyter van Steveninck R. Adaptive rescaling maximizes information transmission. Neuron 2000; 26: 695–702
   PubMed Web of Science ®Google Scholar
• Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation 2001; 14: 325–346
   Web of Science ®Google Scholar
• Brown EN, Kass R, Mitra PP. Multiple neural spike train data analysis: State-of-the-art and future challenges. Nature Neuroscience 2004; 7: 456–461
   PubMed Web of Science ®Google Scholar
• Craven P, Wahba G. Smoothing noise data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik 1979; 31: 377–403
   Web of Science ®Google Scholar
• Czanner G, Eden UT, Wirth S, Yanike M, Suzuki WA, Brown EN. Analysis of between-trial and within-trial neural spiking dynamics. Journal of Neurophysiology 2008; 99: 2672–2693
   PubMed Web of Science ®Google Scholar
• DeAngelis GC, Ohzawa I, Freeman RD. Receptive-field dynamics in the central visual pathways. Trends of Neuroscience 1995; 18: 451–458
   PubMed Web of Science ®Google Scholar
• Escola S, Fontanini A, Katz D, Paninski L. Hidden Markov models for the stimulus-response relationships of multistate neural systems. Neural Computation 2011; 23: 1071–1132
   PubMed Web of Science ®Google Scholar
• Friedman J. Multivariate adaptive regression splines. Annals of Statistics 1991; 19: 1–141
   Web of Science ®Google Scholar
• Gerstein GL, Perkel DH. Simultaneously recorded trains of action potentials: Analysis and functional interpretation. Science 1969; 164: 828–830
   PubMed Web of Science ®Google Scholar
• Haider B, Duque A, Hasenstaub AR, Yu Y, McCormick DA. Enhancement of visual responsiveness by spontaneous local network activity in vivo. Journal of Neurophysiology 2007; 97: 4186–4202
   PubMed Web of Science ®Google Scholar
• Hastie T, Tibshirani R. Generalized Additive Models. Chapman and Hall. 1999
   Google Scholar
• Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd. Springer. 2008
   Google Scholar
• Hoerl A, Kennard R. Ridge regression: Biased estimates for nonorthogonal problems. Technometrics 1970; 12: 55–67
   Web of Science ®Google Scholar
• Kass RE, Ventura V, Brown EN. Statistical issues in the analysis of neuronal data. Journal of Neurophysiology 2005; 94: 8–25
   PubMed Web of Science ®Google Scholar
• Lin Y, Zhang HH. Component selection and smoothing in multivariate nonparametric regression. Annals of Statistics 2006; 34: 2272–2297
   Web of Science ®Google Scholar
• Loader C. Local Regression and Likelihood. Springer. 1999
   Google Scholar
• Machens CK, Wehr MS, Zador AM. Linearity of cortical receptive fields measured with natural sounds. Journal of Neuroscience 2004; 24: 1089–1100
   PubMed Web of Science ®Google Scholar
• Paninski L. Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems 2004; 15: 243–262
   PubMed Web of Science ®Google Scholar
• Paninski L, Pillow JW, Simoncelli EP. Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation 2004; 16: 2533–2561
   PubMed Web of Science ®Google Scholar
• Pillow JW, Simoncelli EP. Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision 2006; 6: 414–428
   PubMed Web of Science ®Google Scholar
• Plourde E, Delgutte B, Brown EN. A point process model for auditory neurons considering both their intrinsic dynamics and the spectrotemporal properties of an extrinsic signal. IEEE Transactions on Biomedical Engineering 2011; 58: 1507–1510
   PubMed Web of Science ®Google Scholar
• Ravikumar P, Lafferty J, Liu H, Wasserman L. Sparse additive models. Journal of the Royal Statistical Society: Series B 2009; 71: 1009–1030
   Google Scholar
• Sahani M, Linden JF. How linear are auditory cortical responses?. Advances in Neural Information Processing Systems, S Becker, S Thrun, K Obermayer, 2003; 15: 125–132
   Google Scholar
• Schaal S, Atkeson CG. Constructive incremental learning from only local information. Neural Computation 1998; 10: 2047–2084
   PubMed Web of Science ®Google Scholar
• Schumaker LL. Spline Functions: Basic Theory. Cambridge Mathematical Library. 2007
   Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 1996; 58: 267–288
   Google Scholar
• Truccolo W, Eden UT, Fellows MR, Donoghue JP, Brown EN. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. Journal of Neurophysiology 2005; 93: 1074–1089
   PubMed Web of Science ®Google Scholar
• Wahba G. Spline Models for Observational Data. SIAM. 1990; 59
   Google Scholar
• Young FW, de Leeuw J, Takane Y. Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features. Psychometrika 1976; 41: 505–529
   Web of Science ®Google Scholar