Code to reproduce the figures and results reported in the following paper are published here along with simulation results and experimental data (all already in the public domain or released with agreement of the authors): **Theory of neural coding predicts an upper bound on estimates of memory variability. Taylor R & Bays PM. Psychological Review. Advance online publication (2020). http://dx.doi.org/10.1037/rev0000189** Code and data is in MATLAB format. File "run_order.m" illustrates a complete run through including simulations (note that the latter were run on a high-performance computing cluster: outputs are included in this release as MAT files). Figure 5 is not included as it only collates results published elsewhere. This release incorporates functions previously made available for fitting the [normal+uniform(+swaps) model][1] (Zhang & Luck, 2008; Bays et al., 2009) and the [population coding model][2] (Bays, 2014), as well as summary data from Ecker et al. (2010, 2011) released by the [Bethge lab][3] (reproduced here under a [CC BY-NC-SA 3.0][4] licence). [1]: http://www.bayslab.com/code/JV10 [2]: http://www.bayslab.com/code/JN14 [3]: http://bethgelab.org/code/ecker2011/ [4]: https://creativecommons.org/licenses/by-nc-sa/3.0/