Parallelizing large networks in NEURON (Lytton et al. 2016)

Bezaire MJ, Soltesz I. (2013). Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity. Hippocampus. 23 [PubMed]

Brette R et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience. 23 [PubMed]

• Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) [Model]
• Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018) [Model]

Cornelis H, Rodriguez AL, Coop AD, Bower JM. (2012). Python as a federation tool for GENESIS 3.0. PloS one. 7 [PubMed]

Davison AP et al. (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in neuroinformatics. 2 [PubMed]

Davison AP, Hines ML, Muller E. (2009). Trends in programming languages for neuroscience simulations. Frontiers in neuroscience. 3 [PubMed]

Diesmann M, Gewaltig M-O. (2007). NEST (Neural Simulation Tool) Scholarpedia. 2

Djurfeldt M, Davison AP, Eppler JM. (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Frontiers in neuroinformatics. 8 [PubMed]

Djurfeldt M et al. (2010). Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics. 8 [PubMed]

Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO. (2008). PyNEST: A Convenient Interface to the NEST Simulator. Frontiers in neuroinformatics. 2 [PubMed]

Gardner D et al. (2003). Towards effective and rewarding data sharing. Neuroinformatics. 1 [PubMed]

Goodman DF, Brette R. (2009). The brian simulator. Frontiers in neuroscience. 3 [PubMed]

HODGKIN AL, HUXLEY AF. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology. 117 [PubMed]

• Squid axon (Hodgkin, Huxley 1952) (NEURON) [Model]
• Squid axon (Hodgkin, Huxley 1952) (SNNAP) [Model]
• Squid axon (Hodgkin, Huxley 1952) (LabAXON) [Model]
• Squid axon (Hodgkin, Huxley 1952) (SBML, XPP, other) [Model]
• Squid axon (Hodgkin, Huxley 1952) used in (Chen et al 2010) (R language) [Model]

Helias M et al. (2012). Supercomputers ready for use as discovery machines for neuroscience. Frontiers in neuroinformatics. 6 [PubMed]

Hines M, Kumar S, Schürmann F. (2011). Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer. Frontiers in computational neuroscience. 5 [PubMed]

• Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011) [Model]

Hines ML, Carnevale NT. (2001). NEURON: a tool for neuroscientists. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry. 7 [PubMed]

• Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001) [Model]

Hines ML, Carnevale NT. (2004). Discrete event simulation in the NEURON environment. Neurocomputing. 58-60

• Discrete event simulation in the NEURON environment (Hines and Carnevale 2004) [Model]

Hines ML, Carnevale NT. (2006). The NEURON Book.

Hines ML, Carnevale NT. (2008). Translating network models to parallel hardware in NEURON. Journal of neuroscience methods. 169 [PubMed]

• Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008) [Model]

Hines ML, Davison AP, Muller E. (2009). NEURON and Python. Frontiers in neuroinformatics. 3 [PubMed]

• NEURON + Python (Hines et al. 2009) [Model]

Hines ML, Eichner H, Schürmann F. (2008). Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. Journal of computational neuroscience. 25 [PubMed]

• Cell splitting in neural networks extends strong scaling (Hines et al. 2008) [Model]

Hines ML, Morse TM, Carnevale NT. (2007). Model structure analysis in NEURON : toward interoperability among neural simulators. Methods in molecular biology (Clifton, N.J.). 401 [PubMed]

Izhikevich EM. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting.

• Artificial neuron model (Izhikevich 2003, 2004, 2007) [Model]

Linton MA, Vissides JM, Calder PR. (1989). Composing user interfaces with interviews Computer. 22

Lytton WW. (2006). Neural Query System: Data-mining from within the NEURON simulator. Neuroinformatics. 4 [PubMed]

• Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006) [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]

Lytton WW, Hines M. (2004). Hybrid neural networks--combining abstract and realistic neural units. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 2004 [PubMed]

Lytton WW, Hines ML. (2005). Independent variable time-step integration of individual neurons for network simulations. Neural computation. 17 [PubMed]

• Local variable time step method (Lytton, Hines 2005) [Model]

Lytton WW, Neymotin SA, Hines ML. (2008). The virtual slice setup. Journal of neuroscience methods. 171 [PubMed]

• The virtual slice setup (Lytton et al. 2008) [Model]

Lytton WW, Stewart M. (2007). Data mining through simulation. Methods in molecular biology (Clifton, N.J.). 401 [PubMed]

McDougal RA, Bulanova AS, Lytton WW. (2016). Reproducibility in Computational Neuroscience Models and Simulations. IEEE transactions on bio-medical engineering. 63 [PubMed]

McDougal RA, Hines ML, Lytton WW. (2013). Reaction-diffusion in the NEURON simulator. Frontiers in neuroinformatics. 7 [PubMed]

• Reaction-diffusion in the NEURON simulator (McDougal et al 2013) [Model]

Migliore M, Cannia C, Lytton WW, Markram H, Hines ML. (2006). Parallel network simulations with NEURON. Journal of computational neuroscience. 21 [PubMed]

• Parallel network simulations with NEURON (Migliore et al 2006) [Model]

Ray S, Bhalla US. (2008). PyMOOSE: Interoperable Scripting in Python for MOOSE. Frontiers in neuroinformatics. 2 [PubMed]

• Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008) [Model]

Salmon J, Moraes M, Dror R, Shaw D. (2011). Parallel random numbers: As easy as 1,2,3. Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis.

Schmitt O, Eipert P. (2012). neuroVIISAS: approaching multiscale simulation of the rat connectome. Neuroinformatics. 10 [PubMed]

Sivagnanam S et al. (2015). Early experiences in developing and managing the neuroscience gateway. Concurrency and computation : practice & experience. 27 [PubMed]

Sneddon MW, Faeder JR, Emonet T. (2011). Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nature methods. 8 [PubMed]

Yamazaki T et al. (2011). Simulation Platform: a cloud-based online simulation environment. Neural networks : the official journal of the International Neural Network Society. 24 [PubMed]

Zenke F, Gerstner W. (2014). Limits to high-speed simulations of spiking neural networks using general-purpose computers. Frontiers in neuroinformatics. 8 [PubMed]

Crone JC et al. (2019). Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator Frontiers in Neuroinformatics. 13

• Large scale neocortical model for PGENESIS (Crone et al 2019) [Model]
• ACnet23 primary auditory cortex model (Beeman et al 2019) [Model]

Dura-Bernal S et al. (2023). Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell reports. 42 [PubMed]

• Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics (Dura-Bernal et al., 2023) [Model]

Lytton WW et al. (2017). Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue). 61(2/3)

• Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017) [Model]