The model and other computer code for the paper:

Huang CH, Huang YT, Chen CC, Chan CK. Propagation and synchronization
of reverberatory bursts in developing cultured networks.
J Comput Neurosci. 2016

is available online. Here is the papers "Implementation" section with
links provided to the resources listed in the references rather than
the references of the paper as originally set in the paper (see the
paper for those):

"We implemented the computational model in the C++ programming
language using the Common Simulation Tools
framework (Chen 2016a). The simulation codes and the
framework are available on the github (Chen 2016b). A
brief description of the structure of the code, the data file
of the simulated system, and animated propagations for a
simulated and an experimental burst are included in the Supplementary
Materials of the paper. The spike data from the
MEA recordings as well as the computer simulations were
processed with the Python3 programming language and
most of the data plots were produced using the Matplotlib
library module. A Jupyter Notebook containing the Python3
codes for data processing and plotting is also included in the
Supplementary Materials."

Paper Abstract:
Developing networks of neural systems can exhibit spontaneous,
synchronous activities called neural bursts, which can be important in
the organization of functional neural circuits. Before the network
matures, the activity level of a burst can reverberate in repeated
rise-and-falls in periods of hundreds of milliseconds following an
initial wave-like propagation of spiking activity, while the burst
itself lasts for seconds. To investigate the spatiotemporal structure
of the reverberatory bursts, we culture dissociated, rat cortical
neurons on a high-density multi-electrode array to record the dynamics
of neural activity over the growth and maturation of the network. We
find the synchrony of the spiking significantly reduced following the
initial wave and the activities become broadly distributed
spatially. The synchrony recovers as the system reverberates until the
end of the burst. Using a propagation model we infer the spreading
speed of the spiking activity, which increases as the culture ages. We
perform computer simulations of the system using a physiological model
of spiking networks in two spatial dimensions and find the parameters
that reproduce the observed resynchronization of spiking in the
bursts. An analysis of the simulated dynamics suggests that the
depletion of synaptic resources causes the resynchronization. The
spatial propagation dynamics of the simulations match well with
observations over the course of a burst and point to an interplay of
the synaptic efficacy and the noisy neural self-activation in
producing the morphology of the bursts.