pymaid.connection_density

pymaid.connection_density(s, t, method='MEDIAN', normalize='DENSITY', remote_instance=None)[source]

Calculate connection density.

Given source neuron(s) s and a target neuron t, calculate the local density of connections as function of the geodesic distance between the postsynaptic contacts on target neuron t.

The general idea here is that spread out contacts might be more effective in depolarizing dendrites of the postsynaptic neuron than highly localized ones. See Gouwens and Wilson, Journal of Neuroscience (2009) for example.

Parameters
  • s (skeleton ID | CatmaidNeuron | CatmaidNeuronList) – Source and target neuron, respectively. Multiple sources are allowed. Target must be single neuron. If t is a CatmaidNeuron, will use connectors and total cable to this neuron. Use to subset density calculations to e.g. the dendrites.

  • t (skeleton ID | CatmaidNeuron | CatmaidNeuronList) – Source and target neuron, respectively. Multiple sources are allowed. Target must be single neuron. If t is a CatmaidNeuron, will use connectors and total cable to this neuron. Use to subset density calculations to e.g. the dendrites.

  • method ('SUM' | 'AVERAGE' | 'MEDIAN', optional) – Arithmetic method used to collapse pairwise geodesic distances over all synaptic contacts on t from s into connection density D.

  • normalize ('DENSITY' | 'CABLE' | False) –

    Normalization method:

    • DENSITY: normalize by synapse density over all postsynapses of the target

    • CABLE: normalize by total cable length of the target

    • False: no normalization

  • remote_instance (CatmaidInstance, optional) –

Returns

connection density – Will return None if no connections or if only a single connection between source and target.

Return type

float