This dataset contains the input data, as well as the analyzed data that our preprint Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies to be found on bioRxiv is based on. The input data (input_data.zip) contains everything that is needed to run the full analysis pipeline from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (notebooks.zip) to generate the figures. Overview An overview image can be found here Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the github repository). Red rectangles denote configuration files (that are part of this dataset and also in the github repository). Contained file types and their structure Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the repository, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the readme. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest. To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (root is where you placed the code from our repository and can be any location on your file system): Configuration files: Part of the repository. Placed into root/working_dir/configs Input data: Place into root/working_dir/data, then unzip in place input_data.zip -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: For details, see readme Analyzed data: Place into root/working_dir/data, then unzip in place classifier_features_results.zip -- Output of the "classifier" step. Results of stimulus classification on the data in features.zip classifier_manifold_results.zip -- Output of the "classifier" step. Results of stimulus classification on the data in extracted_components.zip community_database.zip -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model extracted_components.zip -- Output of "manifold_analysis". Results of factor analysis on the spike times in the input_data features.zip -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the manuscript split_spike_trains.zip -- Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation structural_parameters.zip -- Output of "Structural tribe analysis". Values for the topological parameters in community_database.zip associated with the neuron samples specified in tribes.zip structural_parameters_vol.zip -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples. triads.zip -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in tribes.zip. tribes.zip -- Output of "sample_tribes". Specific neuron samples that are then analyzed further. Notebooks: Place into root/notebooks and unzip in place notebooks.zip -- Jupyter notebooks. Run them to generate the figures in the manuscript.

Generation of this dataset was supported by funding to the Blue Brain Project, a research center of the Ecole polytechnique federale de Lausanne (EPFL), from the Swiss government's ETH Board of the Swiss Federal Institutes of Technology. Levi is supported by an EPSRC grant EP/P025072/ and a collaboration agreement with Ecole Polytechnique Federale de Lausanne.