target_nodes

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target_nodes#

class brainpy.state.spatial.target_nodes(sim, source, target)[source]#

Realized target indices of each source node (NEST GetTargetNodes).

Reads the built network’s adjacency back out (via get_connections()) and groups the realized target indices by source node.

Parameters:

  • sim (Simulator) – The simulator holding the realized connections.

  • source (NodeView) – A single-segment source view; targets are grouped per node in this view’s order.

  • target (NodeView) – The candidate-target population view.

Returns:

One entry per source node (in source order): the sorted unique population-local target indices that node connects to.

Return type:

list of numpy.ndarray

See also

target_positions

the same query returning target coordinates.

Examples

>>> from brainpy import state as bp
>>> import brainunit as u
>>> sim = bp.Simulator(dt=0.1 * u.ms)
>>> pop = sim.create(bp.iaf_psc_alpha, positions=bp.spatial.grid([3, 1], extent=[3.0, 1.0]))
>>> _ = sim.connect(pop, pop,
...     rule=bp.spatial.spatial_pairwise_bernoulli(p=1.0, mask=bp.spatial.circular(1.2)),
...     weight=1.0 * u.pA, delay=1.0 * u.ms)
>>> [t.tolist() for t in bp.spatial.target_nodes(sim, pop, pop)]
[[0, 1], [0, 1, 2], [1, 2]]
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