❭ API reference ❭ singlecell_input_mapper ❭

udvary2022

Top-level pipeline to map synapses onto a postsynaptic cell according to Udvary et al. [12].

This module provides a full pipeline for creating dense connectome models of the rat barrel cortex, based on methods and data presented in Udvary et al. [12] and Egger et al. [13].

Inputs:

  • Morphology of the postsynaptic neuron

  • 3D density field of synapses across the entire neuropil.

  • Number of cells per cell type in the neuropil.

  • Connections spreadsheet containing Post-Synaptic Targets (PSTs) per unit of length and area

  • Bouton locations of individual axon tracings per presynaptic cell type.

This module then uses NetworkMapper to assign synapses to a single post-synaptic cell morphology, based on the inputs mentioned above. This happens according to the following pipeline:

  1. The bouton density field and PST density fields are converted to scalar fields with defined voxel resolution.

  2. Calculates the overlap between these voxels and the dendrites of the postsynaptic neuron morphology using Liang-Barsky clipping [14]. Only these voxels are further considered for potential synapses.

  3. Calculates a synapse density field by multiplying the bouton density field with the PST density fields at these voxels.

  4. Normalizes the previous synapse density fields using cell-type specific PST length/area constraints and the number of cells per cell type.

  5. Poisson samples synapses from this normalized synapse density field to realize synapses. These are randomly placed onto the dendritic branch within that voxel. One such sample is called an “anatomical realization”.

  6. (optional) Repeat steps 4 and 5 to create a collection of anatomical realizations.

Density meshes are accessed using ScalarField. SynapseMapper makes use of SynapseDensity for steps 2, 3 and 4, and finalizes step 5 by itself.

Primary output:

  • Synapse location (.syn) and connectivity (.con) file compatible with ISF.

Additional outputs:

  • summary file containing information about number and presynaptic type and column of anatomical synapses

  • AmiraMesh landmark file containing 3D synapse locations of anatomical synapses of each presynaptic type and column

Functions

map_singlecell_inputs(cellName, cellTypeName, nrOfSamples, numberOfCellsSpreadsheetName, connectionsSpreadsheetName, ExPSTDensityName, InhPSTDensityName, boutonDensityFolderName)

Map inputs to a single cell morphology.

Attributes

logger

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Modules

cell

Classes for setting up a cell morphology and mapping synapses onto it.

generate_nr_of_cells_spreadsheet

Convert a .con file to a dataframe with the number of connected presynaptic cells for each celltype and column.

network_embedding

Create anatomical realizations of connectivity.

reader

Read in hoc files, Amira Mesh files, and spreadsheets with connection probabilities.

scalar_field

Implementation of 3D scalar fields based on numpy arrays.

synapse_mapper

This module allows to compute synapse density fields from a Post-Synaptic Target (PST) density field and assign synapses to a neuron morphology based on this synapse density field.

writer

Write out the results of a single connectivity realization or a population of realizations.