Our lab has been dedicated to the dissemination of open-source software since 1992. Almost all of this software is for use with the NEURON simulation system. Most of the software is from specific models, both from our papers and from those of others posted on ModelDB. We also offer some analysis tools hosted on SimToolDB and simulation management tools on this website.
Molecular level - RxD for NEURON
Computational neuroscience has traditionally focused on electrophysiology, neglecting the accompanying chemophysiology that underlies neural function and the brain’s role as a complex organ within which neuronal networks are embedded. The NEURON rxd module expands the modeling capabilities of the NEURON simulation framework from the electrophysiological components of neurites, cells and networks into the chemophysiological scales of spines, subcellular organelles, interactomics, metabolomics, proteomics. Working in collaboration with Carl Tropper at McGill University we have developed a ‘plugin’ to support stochastic reaction-diffusion; these probabilistic events are essential when the number of molecules is relatively small, as is the case with calcium in a dendritic spine.
The recent expansion of the rxd module into the domain of extracellular space (ECS) considerably extends the scope of chemophysiology into the vast expanse of interneuronal space. Most neuronal network simulations have effectively operated in a vacuum, omitting the effects of nonsynaptic neuromodulators, neuromodulatory gases, ions metabolites, et cetera. These physiological agents also play pathophysiological roles, for example, excessive ion concentrations are seen in spreading depression, and a lack of metabolites can cause tissue damage in ischemia and stroke. The ECS simulation developed here will provide the broadest spatial scale for future multiscale models that will add additional methods at smaller scales.
The rxd module has a GUI and simple Python interface, where the modeler specifies:
1. Regions: where the intracellular, extracellular or subcellular regions being modeled.
2. Species: who, the molecules involved.
3. Transformations: what, the reactions between species, or transits across a membrane.
We continue to work to improve performance without comprising usability. Simulation time has been reduced by moving the runtime methods to compiled C code with Just-In-Time compiled reactions and by adding support for multithread and multiprocessor parallelization. We are working to improve visualization by developing a new GUI toolkit. Support for new and experienced users is provided via the NEURON forum.
Network level - NetPyNE for NEURON
Transforming experimental data into solid conclusions and theory requires integrating and interpreting disparate datasets at multiple scales. The BRAIN Initiative 2025 report highlights this requires rigorous theory and modeling. The widely used NEURON simulator allows researchers to develop biophysically realistic models of neurons and networks. However, building and running parallel simulations of complex brain networks usually requires years of highly technical training. Here we present NetPyNE (www.netpyne.org), a tool that extends NEURON's capabilities and makes it accessible to the wider scientific community.
NetPyNE provides both a programmatic and graphical interface that facilitates the definition, parallel simulation and analysis of data-driven multiscale models. Users can provide specifications at a high level via its standardized declarative language, e.g. a probability of connection, instead of millions of explicit cell-to-cell connections. With a single command, NetPyNE can then generate the NEURON network model and run an efficiently parallelized simulation. The user can then select from a variety of built-in functions to visualize and analyse the results, including connectivity matrices, voltage traces, raster plot, local field potential spectra or information transfer measures. The graphical user interface (https://github.com/MetaCell/NetPyNE-UI) was developed using state-of-the-art technology and allows users to more intuitively access all NetPyNE functionalities: specifying model parameters using drop-down lists or autocomplete forms, interactively visualizing the 3D network, running parallel simulations or plotting results. NetPyNE models can be imported/exported to NeuroML specifications, facilitating model sharing and simulator interoperability.
NetPyNE's standardized format clearly separates model parameters from implementation and can be exported/imported to NeuroML, thus making it easier to understand, reproduce, reuse and share models. This has motivated the conversion of several published models to NetPyNE specifications, including the Potjans & Diesmann cortical network, the Traub thalamocortical network, the Cutsuridis CA1 microcircuit and the Tejada dentate gyrus network. The tool is also being used to develop a variety of new models exploring mouse M1 microcircuits , the claustrum network, cerebellum circuits, transcranial magnetic stimulation (TMS) in cortex, or the underlying biophysics of EEG recordings. We expect the NetPyNE tool to make data-driven biophysically-detailed network modeling accessible to a wider range of researchers and students, including those with limited programming experience, and encourage further collaboration between experimentalists and modelers.
Models and tools
Below are lists of our models and tools divided into the following categories:
- Models from our papers
- Simulation and analysis tools
- Small NEURON scripts
- Models implemented from papers of others
In addition, there are simulations and exercises available for use with the textbook From Computer to Brain.
Models from our papers
Virtually all of our modeling papers are distributed along with source code via ModelDB.
- Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) from Kerr CC, Van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW. Cortical information flow in Parkinson's disease: a composite network/field model. Front Comput Neurosci 2013 7:39.
- Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr et al. 2012) from Kerr CC, Neymotin SA, Chadderdon G, Fietkiewicz C, Francis JT, Lytton WW. Electrostimulation as a Prosthesis for Repair of Information Flow in a Computer Model of Neocortex. IEEE Trans Neural Syst Rehabil Eng 2012 20:153-160.
- Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) from Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19-75 [PubMed]
- Computational Surgery (Lytton et al. 2011) from Lytton WW, Neymotin SA, Wester JC, Contreras D (2011) Neocortical simulation for epilepsy surgery guidance: Localization and intervention Computational Surgery and Dual Training in press.
- Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011) from Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW (2011) Ketamine disrupts theta modulation of gamma in a computer model of hippocampus Journal of Neuroscience, 31:11733-11743
- Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) from Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2010) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci [PubMed]
- Broadening of activity with flow across neural structures (Lytton et al. 2008) from Lytton WW, Orman R, Stewart M (2008) Broadening of activity with flow across neural structures. Perception 37:401-7 [PubMed]
- Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) from Lytton WW, Omurtag A (2007) Tonic-clonic transitions in computer simulation. J Clin Neurophysiol 24:175-81 [PubMed] Neural Comput 20(11):2745-56 [PubMed]
- Signal integration in LGN cells (Briska et al 2003) from Briska AM, Uhlrich DJ, Lytton WW (2003) Computer model of passive signal integration based on whole-cell in vitro studies of rat lateral geniculate nucleus. Eur J Neurosci 17:1531-41[PubMed]
- Hippocampus temporo-septal engram shift model (Lytton 1999) from Lytton WW, Lipton P (1999) Can the hippocampus tell time? The temporo-septal engram shift model. Neuroreport 10:2301-6 [PubMed]
- Thalamic interneuron multicompartment model (Zhu et al. 1999) from Zhu JJ, Lytton WW, Xue JT, Uhlrich DJ (1999) An intrinsic oscillation in interneurons of the rat lateral geniculate nucleus. J Neurophysiol 81:702-11 [PubMed]
Zhu JJ, Uhlrich DJ, Lytton WW (1999) Burst firing in identified rat geniculate interneurons. Neuroscience 91:1445-60 [PubMed]
- Feedforward heteroassociative network with HH dynamics (Lytton 1998) from Lytton WW (1998) Adapting a feedforward heteroassociative network to Hodgkin-Huxley dynamics. J Comput Neurosci 5:353-64 [PubMed]
- Thalamic quiescence of spike and wave seizures (Lytton et al 1997) from 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. J Neurophysiol 77:1679-96 [PubMed]
- Computer model of clonazepam`s effect in thalamic slice (Lytton 1997) from Lytton WW (1997) Computer model of clonazepam's effect in thalamic slice. Neuroreport 8:3339-43 [PubMed]
Simulation and analysis tools
The following are simulation or analysis tools. Analysis tools may be utilized for physiological or simulation data. Note that the posted versions of these tools are not always kept current; contact us for current versions if you want to use them.
- NetPyNE: a Python package to facilitate the development, parallel simulation and analysis of biological neuronal networks using the NEURON simulator.
- Measuring the quality of neuronal identification in ensemble recordings (2011) from Neymotin SA, Lytton WW, Olypher AO, Fenton AA (2011) Measuring the quality of neuronal identification in ensemble recordings. J Neurosci 31(45):16398-16409[PubMed]
- Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010) from Omurtag A, Lytton WW (2010) Spectral method and high-order finite differences for the nonlinear cable equation. Neural Comput 22:2113-36[PubMed]
- The virtual slice setup (Lytton et al. 2008) from Lytton WW, Neymotin SA, Hines ML (2008) The virtual slice setup. J Neurosci Methods 171:309-15 [PubMed]
- JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) from Lytton WW, Omurtag A, Neymotin SA, Hines ML (2008) Just in time connectivity for large spiking networks (partially incorporated into NEURON simulator)
- NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2007) from Neymotin S, Uhlrich DJ, Manning KA, Lytton WW (2008) Data mining of time-domain features from neural extracellular field data Applic. of Comput. Intel. in Bioinf. and Biomed.: Current Trends and Open Problems 151:119-140
- Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006) from Lytton WW (2006) Neural Query System: Data-mining from within the NEURON simulator. Neuroinformatics 4:163-76 [PubMed]
- Parallel network simulations with NEURON (Migliore et al 2006) from Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [PubMed] (incorporated into NEURON simulator)
- Local variable time step method (Lytton, Hines 2005) from Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21 [PubMed] (incorporated into NEURON simulator)
- SIMCTRL: Simulation Control for the NEURON simulator in GNU Emacs (documentation only; packages available on request since not encapsulated)
- GRVEC:: Graphical user interface for vector manipulation in NEURON (documentation only; packages available on request since not encapsulated)
Small NEURON scripts
These are mostly minor helper functions for NEURON. Note that some of these tools are now deprecated since similar or identical tools can now be accessed via NEURON's PYTHON interface.
- Neural fingerprinting using NQS
- Genetic algorithm from NEURON NEURON interface to GAUL, a genetic algorithm library
- Hoc filter tool Low-pass filtering using NEURON's fft() routine (obsolete: use PYTHON)
- Embed hoc files Follow load_files() and xopen() commands in hoc to create a properly embedded single hoc file.
- Summed synapse mechanism A standard #INCLUDE postsynaptic mechanism for NMODL to utilize summed synapse mechanism based on Lytton WW. Optimizing synaptic conductance calculation for network simulations. Neural Comput. 1996;8:501-9.
- A timer
- Union template in hoc
- Sample Entropy
- Follow HOC_LIBRARY_PATH to find routines.
We depend on the kindness of strangers in developing our simulations and post models of others that we have successfully ported to NEURON (yes, there were a few that were not entirely successful -- i.e. couldn't quite replicate the figures)
- Thalamocortical augmenting response from Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Computational models of thalamocortical augmenting responses. J Neurosci 18:6444-65 [PUBMED]
- CA3 pyramidal cell: rhythmogenesis in a reduced Traub model from Pinsky PF, Rinzel J (1994) Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J Comput Neurosci 1:39-60 [PUBMED]
- Artificial neuron model from Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569-72[PUBMED] ]
- Excitatory and inhibitory interactions in populations of model neurons from Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1-24 [PUBMED]
- Gamma oscillations in hippocampal interneuron networks from Wang XJ, Buzsaki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 16:6402-13 [PUBMED]
- Cortical network model of posttraumatic epileptogenesis(Bush et al 1999) from Bush PC, Prince DA, Miller KD (1999) Increased pyramidal excitability and NMDA conductance can explain posttraumatic epileptogenesis without disinhibition: a model. J Neurophysiol 82:1748-58 [PUBMED] (reprised for parallel simulation (parbush))