VanillaProj#
- class brainpy.dyn.VanillaProj(comm, out, post, name=None, mode=None)[source]#
Synaptic projection which defines the synaptic computation with the dimension of pre-synaptic neuron group.
Code Examples
To simulate an E/I balanced network model:
class EINet(bp.DynSysGroup): def __init__(self): super().__init__() self.N = bp.dyn.LifRef(4000, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5., V_initializer=bp.init.Normal(-55., 2.)) self.delay = bp.VarDelay(self.N.spike, entries={'I': None}) self.syn1 = bp.dyn.Expon(size=3200, tau=5.) self.syn2 = bp.dyn.Expon(size=800, tau=10.) self.E = bp.dyn.VanillaProj(comm=bp.dnn.JitFPHomoLinear(3200, 4000, prob=0.02, weight=0.6), out=bp.dyn.COBA(E=0.), post=self.N) self.I = bp.dyn.VanillaProj(comm=bp.dnn.JitFPHomoLinear(800, 4000, prob=0.02, weight=6.7), out=bp.dyn.COBA(E=-80.), post=self.N) def update(self, input): spk = self.delay.at('I') self.E(self.syn1(spk[:3200])) self.I(self.syn2(spk[3200:])) self.delay(self.N(input)) return self.N.spike.value model = EINet() indices = bm.arange(1000) spks = bm.for_loop(lambda i: model.step_run(i, 20.), indices) bp.visualize.raster_plot(indices, spks, show=True)
- Parameters:
comm (
DynamicalSystem
) – The synaptic communication.out (
JointType
[DynamicalSystem
,BindCondData
]) – The synaptic output.post (
DynamicalSystem
) – The post-synaptic neuron group.