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Model building overview

Extracting models from existing connectomes or related data.

File Description Example
/conn_prob.py Connection probability model
  • 'order': 1
1st order model [Gal et al. 2020]: Erdős-Rényi (constant)
  • 'order': 2
2nd order model [Gal et al. 2020]: Distance dependent model (exponential fit)

ℹ️ Position mapping supported!		 Distance dependent connection probability model
  • 'order': 3
3rd order model [Gal et al. 2020]: Bipolar distance dependent model (exponential fit)

ℹ️ Position mapping supported!		 Bipolar distance dependent connection probability model
  • 'order': 4
4th order model [Gal et al. 2020]: Offset dependent model based on linear interpolation

ℹ️ Position mapping supported!		 Offset dependent connection probability model
  • 'order': '4R'
4th order reduced model [Modified from Gal et al. 2020]: Offset dependent model (linear interpolation), reduced to radial (Δr; instead of Δx, Δy) and axial (Δz) offsets

ℹ️ Position mapping supported!		 Reduced offset dependent connection probability model
  • 'order': 5
5th order model [Gal et al. 2020]: Position dependent model based on linear interpolation

ℹ️ Position mapping supported!		 Position dependent connection probability model
  • 'order': '5R'
5th order reduced model [Modified from Gal et al. 2020]: Position dependent model (linear interpolation), reduced to axial position (z; instead of x, y, and z) and radial (Δr; instead of Δx, Δy) and axial (Δz) offsets

ℹ️ Position mapping supported!		 Reduced position dependent connection probability model
/conn_prob_adj.py Adjacency model
Deterministic connection probability model (containing only probabilities 0.0 or 1.0) based on an adjacency matrix between pre- and post-synaptic neurons.		 Adjacency model
/delay.py Axonal delay model
Model for linear distance-dependent axonal delays between soma of source neuron and synapse location on target dendrite.		 Linear axonal delay model
/conn_props.py Connection properties model
Interpolation model for synaptic property distributions (e.g., conducance, #synapses/connection, depression time, facilitation time, ...) of connections between pairs of source/target neurons belonging to different m-types.		 Connection properties model (conductance)
/pos_mapping.py Position mapping model
Neuron position mapping from atlas space to flat space.

ℹ️ Blue Brain - Voxcell required!		 Position mapping model (flat space)
/pos_mapping_from_table.py Position mapping model (from table)
Neuron position mapping loaded from pre-computed table (pandas DataFrame).		 Position mapping model from table

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