NeuronetExperimenter Manual
J. A. Hayes
and J. L. Mendenhall
Updated: 7/25/2025
Download the latest NeuronetExperimenter source
code
The 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.
NeuronetExperimenter 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:
Installation Guide
Tutorials
Tutorial #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. Adding Additional Types of Neurons to a Network
Tutorial
#6. Writing Custom Utility Scripts
Tutorial
#7. Exporting Models as LaTex
Tutorial #8. Implementing
Integrate-and-Fire Models
Basic Topics
Parallel Processing of
a Network Simulation
Sampling of Data Output
Using the Utility Scripts
nne
Python Package Documentation
Advanced Topics
The Build Process
Additional Information
File 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
eLIFE 2014;3:e03427.
* 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.