J. A. Hayes and J. L. Mendenhall
Download the latest NeuronetExperimenter source code
NeuronetExperimenter software can be used to quickly simulate large
sets of biological neurons arranged with arbitrary network
connectivity. The software makes it easy to investigate the behaviors of large,
complex, neural networks, especially when starting from XPPAUT models. The software is very flexible and
allows users to develop multiple neuron types with different
constituent differential equations describing their behavior. Any of
these neuron types can be included in a network together where each
neuron has its own unique set of parameters that can be changed during
the course of the simulation. The software also includes extensive analysis features useful for studying the behaviors of large networks. See a list of known peer-reviewed publications that use the software here.
can run simulations serially (i.e., on a single CPU), or in parallel
(i.e., on multi-CPU machines or clusters of computers). For the latter
case, simulation integrations are performed in parallel, so special
considerations are required to understand what types of networks will
benefit from parallel processing (see the Parallel Processing of a Network Simulation topic).
The easiest way to get started with this simulator is to walk through the installation guide and then follow the tutorials:
TutorialsTutorial #1. Building and Running a Simple Single-Neuron Simulation
Tutorial #2. Coupling Neurons through Synaptic Connections
Tutorial #3. Changing Neuronal Parameters Before and During a Simulation
Tutorial #4. Creating Larger Networks and Introducing Parameter Heterogeneity and Variable Connectivity
Tutorial #5. Analysis of Burst Activity
Tutorial #6. Adding Additional Types of Neurons to a Network
Tutorial #7. Writing Custom Utility Scripts
Tutorial #8. Building Multi-compartment Neurons
Basic TopicsParallel Processing of a Network Simulation
Sampling of Data Output
Using the Utility Scripts
The Network Builder Script
Useful Environment Variables to Set
Advanced TopicsAdvanced Installation Guide
The Build Process
Network Burst Finding algorithm
Additional InformationFile Types and Usage.
Peer-reviewed publications which use NeuronetExperimenter
Song, H., Hayes, J. A., Vann, N. C., Wang, X., LaMar, M. D., and Del Negro C. A. (2016). Functional interactions between mammalian respiratory rhythogenic and premotor circuitry
Journal of Neuroscience, July 2016.
Song, H., Hayes, J. A., Vann, N. C., LaMar, M. D., and Del Negro C. A. (2015). Mechanisms leading to rhythm cessation in the respiratory preBötzinger complex due to piecewise cumulative neuronal deletions
eNeuro, Sept. 2015.
Wang, X.*, Hayes, J. A.*, Revill, A., Song, H., Kottick, A., Vann, N.,
LaMar, M. D., Picardo, M., Funk, G.D., and Del Negro, C. A. (2014). Laser
ablation of Dbx1 interneurons in the pre-Bötzinger Complex abolishes
inspiratory rhythm and impairs motor output in neonatal mice
* contributed equally
Rubin, J. E.*, Hayes, J.
A.*, Mendenhall, J. L., and Del Negro, C. A. (2009). Calcium-activated
nonspecific cation current and synaptic depression promote
network-dependent burst oscillations
Proceedings of the National Academy of Science, 106:2939-2944.
* contributed equally
Hayes, J. A., Mendenhall, J. L., Brush, B. R., and Del Negro, C. A. (2008). 4-aminopyridine-sensitive
outward currents in preBötzinger Complex neurons influence respiratory
rhythm generation in neonatal mice
Journal of Physiology (London), 586.7:1921-36.