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.