Abrams AB, Hillis JM, Brainard DH. (2007). The relation between color discrimination and color constancy: when is optimal adaptation task dependent? Neural computation. 19 [PubMed]

Arsiero M, Lüscher HR, Lundstrom BN, Giugliano M. (2007). The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience. 27 [PubMed]

• Input Fluctuations effects on f-I curves (Arsiero et al. 2007) [Model]

Asabuki T, Fukai T. (2020). Somatodendritic consistency check for temporal feature segmentation. Nature communications. 11 [PubMed]

• Somatodendritic consistency check for temporal feature segmentation (Asabuki & Fukai 2020) [Model]

Deneve S. (2008). Bayesian spiking neurons II: learning. Neural computation. 20 [PubMed]

Goldman MS. (2004). Enhancement of information transmission efficiency by synaptic failures. Neural computation. 16 [PubMed]

Haas JS, Dorval AD, White JA. (2007). Contributions of Ih to feature selectivity in layer II stellate cells of the entorhinal cortex. Journal of computational neuroscience. 22 [PubMed]

Muscinelli SP, Gerstner W, Schwalger T. (2019). How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. PLoS computational biology. 15 [PubMed]

• Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019) [Model]

Narayanan R, Johnston D. (2007). Long-term potentiation in rat hippocampal neurons is accompanied by spatially widespread changes in intrinsic oscillatory dynamics and excitability. Neuron. 56 [PubMed]

• Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008) [Model]
• Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007) [Model]

Richard A, Orio P, Tanré E. (2018). An integrate-and-fire model to generate spike trains with long-range dependence Journal of Computational Neuroscience.

• Perfect Integrate and fire with noisy adaptation or fractional noise (Richard et al 2018) [Model]

Sharpee T, Rust NC, Bialek W. (2004). Analyzing neural responses to natural signals: maximally informative dimensions. Neural computation. 16 [PubMed]

Shlens J, Kennel MB, Abarbanel HD, Chichilnisky EJ. (2007). Estimating information rates with confidence intervals in neural spike trains. Neural computation. 19 [PubMed]

Snippe HP, van Hateren JH. (2007). Dynamics of nonlinear feedback control. Neural computation. 19 [PubMed]

Spain WJ, Fairhall AL, Lundstrom BN, Famulare M, Sorensen LB. (2009). Sensitivity of firing rate to input fluctuations depends on time scale separation between fast and slow variables in single neurons J Comput Neurosci. in press

• Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009) [Model]

Tripp BP, Eliasmith C. (2010). Population models of temporal differentiation. Neural computation. 22 [PubMed]

• Population models of temporal differentiation (Tripp and Eliasmith 2010) [Model]

Trousdale J, Carroll SR, Gabbiani F, Josić K. (2014). Near-optimal decoding of transient stimuli from coupled neuronal subpopulations. The Journal of neuroscience : the official journal of the Society for Neuroscience. 34 [PubMed]

• Vertical System (VS) tangential cells network model (Trousdale et al. 2014) [Model]

Wang XJ, Liu Y, Sanchez-Vives MV, McCormick DA. (2003). Adaptation and temporal decorrelation by single neurons in the primary visual cortex. Journal of neurophysiology. 89 [PubMed]

• Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003) [Model]

Wennekers T, Ay N. (2005). Finite state automata resulting from temporal information maximization and a temporal learning rule. Neural computation. 17 [PubMed]

Zilany MS, Bruce IC, Nelson PC, Carney LH. (2009). A phenomenological model of the synapse between the inner hair cell and auditory nerve: long-term adaptation with power-law dynamics. The Journal of the Acoustical Society of America. 126 [PubMed]

• Long-term adaptation with power-law dynamics (Zilany et al. 2009) [Model]
• Cochlea: inner ear models in Python (Zilany et al 2009, 2014; Holmberg M 2007) [Model]