navis.arbor_segregation_index

navis.arbor_segregation_index(x)

Per arbor seggregation index (SI).

The segregation index (SI) as established by Schneider-Mizell et al. (eLife, 2016) is a measure for how polarized a neuron is. SI of 1 indicates total segregation of inputs and outputs into dendrites and axon, respectively. SI of 0 indicates homogeneous distribution. Here, we apply this to each arbour within a neuron by asking “If we were to cut a neuron at this node, what would the SI of the two resulting fragments be?”

Parameters:

• x (TreeNeuron | MeshNeuron | NeuronList) – Neuron(s) to calculate segregation indices for. Must have connectors!

• parallel (bool) – If True and input is NeuronList, use parallel processing. Requires pathos.

• n_cores (int, optional) – Numbers of cores to use if parallel=True. Defaults to half the available cores.

• progress (bool) – 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 “segregation_index” as column in the node table (for TreeNeurons) or as .segregation_index property (for MeshNeurons).

Return type:

neuron

Examples

>>> import navis
>>> n = navis.example_neurons(1)
>>> n.reroot(n.soma, inplace=True)
>>> _ = navis.arbor_segregation_index(n)
>>> n.nodes.segregation_index.max().round(3)
0.277

See also

segregation_index()

Calculate segregation score (polarity) between two fragments of a neuron.

synapse_flow_centrality()

Calculate synapse flow centrality after Schneider-Mizell et al.

bending_flow()

Variation on the Schneider-Mizell et al. synapse flow.

split_axon_dendrite()

Split the neuron into axon, dendrite and primary neurite.