Anderson WD, Makadia HK, Vadigepalli R. (2016). Molecular variability elicits a tunable switch with discrete neuromodulatory response phenotypes. Journal of computational neuroscience. 40 [PubMed]

• Cell signaling/ion channel variability effects on neuronal response (Anderson, Makadia, et al. 2015) [Model]

Bhalla US. (2002). Biochemical signaling networks decode temporal patterns of synaptic input. Journal of computational neuroscience. 13 [PubMed]

Cataldo E et al. (2005). Computational model of touch sensory cells (T Cells) of the leech: role of the afterhyperpolarization (AHP) in activity-dependent conduction failure. Journal of computational neuroscience. 18 [PubMed]

• Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007) [Model]

De Schutter E, Smolen P. (1998). Calcium dynamics in large neuronal models Methods In Neuronal Modeling: From Ions To Networks.

Delord B, Baraduc P, Costalat R, Burnod Y, Guigon E. (2000). A model study of cellular short-term memory produced by slowly inactivating potassium conductances. Journal of computational neuroscience. 8 [PubMed]

Destexhe A, Mainen ZF, Sejnowski TJ. (1994). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Journal of computational neuroscience. 1 [PubMed]

• Application of a common kinetic formalism for synaptic models (Destexhe et al 1994) [Model]
• Kinetic synaptic models applicable to building networks (Destexhe et al 1998) [Model]

Goldman MS, Golowasch J, Marder E, Abbott LF. (2001). Global structure, robustness, and modulation of neuronal models. The Journal of neuroscience : the official journal of the Society for Neuroscience. 21 [PubMed]

• Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001) [Model]

Günay C, Prinz AA. (2010). Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks. The Journal of neuroscience : the official journal of the Society for Neuroscience. 30 [PubMed]

• Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004) [Model]

Kanold PO, Manis PB. (2001). A physiologically based model of discharge pattern regulation by transient K+ currents in cochlear nucleus pyramidal cells. Journal of neurophysiology. 85 [PubMed]

• CN pyramidal fusiform cell (Kanold, Manis 2001) [Model]

Le Masson G, Przedborski S, Abbott LF. (2014). A computational model of motor neuron degeneration. Neuron. 83 [PubMed]

Li X et al. (2019). Ca2+-Activated K+ Channels Reduce Network Excitability, Improving Adaptability and Energetics for Transmitting and Perceiving Sensory Information. The Journal of neuroscience : the official journal of the Society for Neuroscience. 39 [PubMed]

• How BK and SK channels benefit early vision (Li X et al 2019) [Model]

Liu Z, Golowasch J, Marder E, Abbott LF. (1998). A model neuron with activity-dependent conductances regulated by multiple calcium sensors. The Journal of neuroscience : the official journal of the Society for Neuroscience. 18 [PubMed]

• Activity dependent conductances in a neuron model (Liu et al. 1998) [Model]

Lytton WW, Contreras D, Destexhe A, Steriade M. (1997). Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. Journal of neurophysiology. 77 [PubMed]

• Thalamic quiescence of spike and wave seizures (Lytton et al 1997) [Model]

Nowotny T, Szücs A, Levi R, Selverston AI. (2007). Models wagging the dog: are circuits constructed with disparate parameters? Neural computation. 19 [PubMed]

Ori H, Marder E, Marom S. (2018). Cellular function given parametric variation in the Hodgkin and Huxley model of excitability. Proceedings of the National Academy of Sciences of the United States of America. 115 [PubMed]

• Cellular function given parametric variation in the HH model of excitability (Ori et al 2018) [Model]

Rudolph M, Destexhe A. (2006). Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies. Neural computation. 18 [PubMed]

Scheler G. (2013). Learning intrinsic excitability in medium spiny neurons F1000Research. 2 [PubMed]

• Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014) [Model]

Sweeney Y, Hellgren Kotaleski J, Hennig MH. (2015). A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks. PLoS computational biology. 11 [PubMed]

• Diffusive homeostasis in a spiking network model (Sweeney et al. 2015) [Model]

Tobin AE, Calabrese RL. (2006). Endogenous and half-center bursting in morphologically inspired models of leech heart interneurons. Journal of neurophysiology. 96 [PubMed]

Wu HY, Baer SM. (1998). Analysis of an excitable dendritic spine with an activity-dependent stem conductance. Journal of mathematical biology. 36 [PubMed]

Yang J, Shakil H, Ratté S, Prescott SA. (2022). Minimal requirements for a neuron to co-regulate many properties and the implications for ion channel correlations and robustness eLife. 11 [PubMed]

• A modified Morris-Lecar model with gM and gAHP (Yang et al., 2022) [Model]

Zang Y, Marder E. (2023). Neuronal morphology enhances robustness to perturbations of channel densities Proceedings of the National Academy of Sciences of the United States of America. 120 [PubMed]

• LP neuron model database (Zang and Marder 2023) [Model]