{'fraction_non_alphanumeric': 0.017470300489168415, 'fraction_numerical': 0.001397624039133473, 'mean_word_length': 6.3061224489795915, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Method for Estimating the Potential Synaptic Connections Between Axons and Dendrites From 2D Neuronal Images.

Computational neuroscience aims to model, reproduce, and predict network dynamics for different neuronal ensembles by distilling knowledge derived from electrophysiological and morphological evidence. However, analyses and simulations often remain critically limited by the sparsity of direct experimental constraints on essential parameters, such as electron microscopy and electrophysiology pair/multiple recording evidence of connectivity statistics. Notably, available data are particularly scarce regarding quantitative information on synaptic connections among identified neuronal types. Here, we present a user-friendly data-driven pipeline to estimate connection probabilities, number of contacts per connected pair, and distances from the pre- and postsynaptic somas along the axonal and dendritic paths from commonly available two-dimensional tracings and other broadly accessible measurements. The described procedure does not require any computational background and is accessible to all neuroscientists. This protocol therefore fills the important gap from neuronal morphology to circuit organization and can be applied to many different neural systems, brain regions, animal species, and data sources. Graphic abstract: The processing protocol from 2D reconstructions to quantitated synaptic connections.