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FullProjDelta

FullProjDelta#

class brainpy.dyn.FullProjDelta(pre, delay, comm, post, name=None, mode=None)[source]#

Full-chain of the synaptic projection for the Delta synapse model.

The synaptic projection requires the input is the spiking data, otherwise the synapse is not the Delta synapse model.

The full-chain means that the model needs to provide all information needed for a projection, including pre -> delay -> comm -> post.

Model Descriptions

\[I_{syn} (t) = \sum_{j\in C} g_{\mathrm{max}} * \delta(t-t_j-D)\]

where \(g_{\mathrm{max}}\) denotes the chemical synaptic strength, \(t_j\) the spiking moment of the presynaptic neuron \(j\), \(C\) the set of neurons connected to the post-synaptic neuron, and \(D\) the transmission delay of chemical synapses. For simplicity, the rise and decay phases of post-synaptic currents are omitted in this model.

Code Examples

import brainpy as bp
import brainpy.math as bm


class Net(bp.DynamicalSystem):
  def __init__(self):
    super().__init__()

    self.pre = bp.dyn.PoissonGroup(10, 100.)
    self.post = bp.dyn.LifRef(1)
    self.syn = bp.dyn.FullProjDelta(self.pre, 0., bp.dnn.Linear(10, 1, bp.init.OneInit(2.)), self.post)

  def update(self):
    self.syn()
    self.pre()
    self.post()
    return self.post.V.value


net = Net()
indices = bm.arange(1000).to_numpy()
vs = bm.for_loop(net.step_run, indices, progress_bar=True)
bp.visualize.line_plot(indices, vs, show=True)
Parameters:
update()[source]#

The function to specify the updating rule.

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