Set network structure — set.network.structure • DACx

This function sets the structure of a network object, defining its layers, columns, neuron types, and local connectivity parameters. It also generates local nodes based on the specified structure.

Usage

set.network.structure(
  network,
  neuron_types = c("principal"),
  hemisphere_names = NULL,
  subcortical_layer_names = NULL,
  layer_names = c("layer"),
  n_hemispheres = 1,
  n_subcortical_layers = 0,
  n_layers = 1,
  n_columns = 1,
  n_patches = 1,
  layer_height = 180,
  column_diameter = 120,
  segment_length = 20,
  hem_separation_factor = 40,
  sub_separation_factor = 20,
  layer_separation_factor = 2.5,
  column_separation_factor = 2.5,
  patch_separation_factor = 2.5,
  synaptic_neighborhood = 10,
  neurons_per_node = 10
)

Arguments

network: Network object to configure.
neuron_types: Character vector giving (names of) types of neurons in the network. Known types can be accessed using print.known.celltypes(). Default is "principal", which will assign the most common neuron type for each layer, as defined in principal.neurons().
hemisphere_names: Character vector of length n_hemispheres giving names for the hemispheres (default: auto-generated as "left" or c("left","right"), depending on if there are one or two hemispheres).
subcortical_layer_names: Character vector of length n_subcortical_layers giving names for the subcortical layers (default: auto-generated as "subL1", "subL2", ...). Must be distinct from all cortical layer names.
layer_names: Character vector giving names of cortical layers in the network, ordered deepest to most superficial, e.g. c("L6", "L5", "L4", "L3", "L2", "L1").
n_hemispheres: Integer giving number of hemispheres; must be 1 or 2 (default: 1).
n_subcortical_layers: Integer giving number of subcortical layers (e.g., thalamic relay nuclei); can be 0 (default: 0).
n_layers: Integer giving number of cortical layers in the network (default: 1).
n_columns: Integer giving number of columns in the network (default: 1).
n_patches: Integer giving the number of "patches" (n_layers x n_columns sheets) in the network. This is the secondarly columnar axis, orthogonal to the laminar and principal columnar axes (default: 1).
layer_height: Numeric giving height of each layer (default: 180.0 micron).
column_diameter: Numeric giving diameter of each column (default: 120.0 micron).
segment_length: Numeric giving expected length of each segment in the axonal and dendritic processes of each neuron (default: 20.0 micron).
hem_separation_factor: Numeric giving distance between hemispheres as a fraction of column diameter (default: 40.0).
sub_separation_factor: Numeric giving distance from the cortical sheet to the first subcortical layer as a fraction of layer height (default: 20.0).
layer_separation_factor: Numeric giving mean distance between layers as a fraction of layer height (default: 2.5).
column_separation_factor: Numeric giving mean distance between columns as a fraction of column diameter (default: 2.5).
patch_separation_factor: Numeric giving mean distance between network patches as a fraction of column diameter (default: 2.5).
synaptic_neighborhood: Numeric giving the radius (in microns) within which an axon node will trigger a synapse when near a dendrite node (default: 10.0). #' @param neurons_per_node Matrix giving mean number of neurons of each type per node in each layer, with cortical layers first and then subcortical layers; dimensions must match n_layers + n_subcortical_layers (rows) and length of neuron_types (columns), or 2 * (n_layers + n_subcortical_layers) if specifying different cell type counts for a second hemisphere. If there are two hemispheres but only n_layers + n_subcortical_layers rows, then the counts are reused for the second hemisphere.

Value

The updated network object with the specified structure and local nodes generated.