navis.arbor_segregation_index¶
- navis.arbor_segregation_index(x)[source]¶
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.