navis.bending_flow¶

navis.bending_flow(x)[source]¶

Calculate synapse “bending” flow.

This is a variation of the algorithm for calculating synapse flow from Schneider-Mizell et al. (eLife, 2016).

The way this implementation works is by iterating over each branch point and counting the number of pre->post synapse paths that “flow” from one child branch to the other(s).

Parameters:: x (TreeNeuron | MeshNeuron | NeuronList) – Neuron(s) to calculate bending flow for. Must have connectors!

Notes

This is algorithm appears to be more reliable than synapse flow centrality for identifying the main branch point for neurons that have incompletely annotated synapses. parallel : bool

If True and input is NeuronList, use parallel processing. Requires pathos.

n_coresint, optional: Numbers of cores to use if parallel=True. Defaults to half the available cores.
progressbool: Whether to show a progress bar. Overruled by navis.set_pbars.
omit_failures :bool: If True will omit failures instead of raising an exception. Ignored if input is single neuron.

Returns:: Adds “bending_flow” as column in the node table (for TreeNeurons) or as .bending_flow property (for MeshNeurons).
Return type:: neuron

Examples

>>> import navis
>>> n = navis.example_neurons(1)
>>> n.reroot(n.soma, inplace=True)
>>> _ = navis.bending_flow(n)
>>> n.nodes.bending_flow.max()
785645