Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity

It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.


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 You should state whether an appropriate sample size was computed when the study was being designed  You should state the statistical method of sample size computation and any required assumptions The current study is based on data collected and published previously (Pertzov et al 2017).The number of participants was selected to match the number of participants in a similar study (Pertzov et al 2013) that used similar methods.The current data is based on previously published studies, and we direct the reader to these studies for further read, but also include in this manuscript how this data is obtained (i.e., how the experiments are performed) in pages 3 and 22.

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Please outline where this information can be found within the submission (e.g., page numbers or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to page numbers in the manuscript.) Additional data files ("source data")  We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table  Where provided, these should be in the most useful format, and they can be uploaded as "Source data" files linked to a main figure or table  Include model definition files including the full list of parameters used  Include code used for data analysis (e.g., R, MatLab)  Avoid stating that data files are "available upon request" eLife Sciences Publications, Ltd is a limited liability non-profit non-stock corporation incorporated in the State of Delaware, USA, with company number 5030732, and is registered in the UK with company number FC030576 and branch number BR015634 at the address 1st Floor, 24 Hills Road, Cambridge CB2 1JP | August 2014 Each of the 10 participants performed between 11 and 15 blocks of 80 trials.Each block consisted of 20 trials for each of the 4 possible item numbers, consisting of 5 trials for each delay duration.No outliers were excluded.These experiments are explained in page 22 of the manuscript.
The data in the manuscript was published in Pertzov et al 2017.We have uploaded this raw data with this submission.Data is reported in Figure 1, where average of responses over subjects and trials along with standard error of the mean values are reported.In the current manuscript the statistical analysis is reported in pages 3 & 4. The exact p-values is reported when p was larger than 0.001.We also used Bayesian Information Criterion (BIC) when comparing different models, and this is described in pages 24, 25, and 36 of the manuscript.