pymaid.predict_connectivity

pymaid.predict_connectivity(source, target, method='possible_contacts', remote_instance=None, **kwargs)[source]

Calculate potential synapses from source onto target neurons.

Based on a concept by Alexander Bates.

Parameters
  • source (CatmaidNeuron | CatmaidNeuronList) – Neuron(s) for which to compute potential connectivity. This is unidirectional: source -> target.

  • target (CatmaidNeuron | CatmaidNeuronList) – Neuron(s) for which to compute potential connectivity. This is unidirectional: source -> target.

  • method ('possible_contacts') – Method to use for calculations. See Notes.

  • **kwargs

    1. For method ‘possible_contacts’:
      • dist to set distance between connectors and nodes manually.

      • n_irq to set number of interquartile ranges of harmonic mean. Default = 2.

Notes

Method possible_contacts:
  1. Calculating harmonic mean of distances d (connector->node) at which onnections between neurons A and neurons B occur.

  2. For all presynapses of neurons A, check if they are within n_irq (default=2) interquartile range of d of a neuron B node.

Neurons without cable or presynapses will be assigned a predicted connectivity of 0.

Returns

Matrix holding possible synaptic contacts. Sources are rows, targets are columns:

         target1  target2  target3  ...
source1    5        1        0
source2    10       20       5
source3    4        3        15
...

Return type

pandas.DataFrame