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Merge branch 'updates' of https://github.com/PKU-NIP-Lab/BrainPy into…
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@chaoming0625
chaoming0625 committed Sep 10, 2023
2 parents 294bc0d + 7369dd9 commit 1ad8ccb
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249 changes: 249 additions & 0 deletions examples/dynamics_simulation/ei_nets.py
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import brainpy as bp
import brainpy.math as bm


def model1():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
self.N = bp.dyn.LifRefLTC(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.E = bp.dyn.ProjAlignPost1(comm=bp.dnn.EventJitFPHomoLinear(3200, 4000, prob=0.02, weight=0.6),
syn=bp.dyn.Expon(size=4000, tau=5.),
out=bp.dyn.COBA(E=0.),
post=self.N)
self.I = bp.dyn.ProjAlignPost1(comm=bp.dnn.EventJitFPHomoLinear(800, 4000, prob=0.02, weight=6.7),
syn=bp.dyn.Expon(size=4000, tau=10.),
out=bp.dyn.COBA(E=-80.),
post=self.N)

def update(self, input):
spk = self.delay.at('I')
self.E(spk[:3200])
self.I(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)


def model2():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
ne, ni = 3200, 800
self.E = bp.dyn.LifRefLTC(ne, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.I = bp.dyn.LifRefLTC(ni, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.E2E = bp.dyn.ProjAlignPost2(pre=self.E,
delay=0.1,
comm=bp.dnn.EventJitFPHomoLinear(ne, ne, prob=0.02, weight=0.6),
syn=bp.dyn.Expon(size=ne, tau=5.),
out=bp.dyn.COBA(E=0.),
post=self.E)
self.E2I = bp.dyn.ProjAlignPost2(pre=self.E,
delay=0.1,
comm=bp.dnn.EventJitFPHomoLinear(ne, ni, prob=0.02, weight=0.6),
syn=bp.dyn.Expon(size=ni, tau=5.),
out=bp.dyn.COBA(E=0.),
post=self.I)
self.I2E = bp.dyn.ProjAlignPost2(pre=self.I,
delay=0.1,
comm=bp.dnn.EventJitFPHomoLinear(ni, ne, prob=0.02, weight=6.7),
syn=bp.dyn.Expon(size=ne, tau=10.),
out=bp.dyn.COBA(E=-80.),
post=self.E)
self.I2I = bp.dyn.ProjAlignPost2(pre=self.I,
delay=0.1,
comm=bp.dnn.EventJitFPHomoLinear(ni, ni, prob=0.02, weight=6.7),
syn=bp.dyn.Expon(size=ni, tau=10.),
out=bp.dyn.COBA(E=-80.),
post=self.I)

def update(self, inp):
self.E2E()
self.E2I()
self.I2E()
self.I2I()
self.E(inp)
self.I(inp)
return self.E.spike

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)


def model3():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
self.N = bp.dyn.LifRefLTC(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)


def model4():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
ne, ni = 3200, 800
self.E = bp.dyn.LifRefLTC(ne, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.I = bp.dyn.LifRefLTC(ni, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.E2E = bp.dyn.ProjAlignPreMg1(pre=self.E,
syn=bp.dyn.Expon.desc(size=ne, tau=5.),
delay=0.1,
comm=bp.dnn.JitFPHomoLinear(ne, ne, prob=0.02, weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.E)
self.E2I = bp.dyn.ProjAlignPreMg1(pre=self.E,
syn=bp.dyn.Expon.desc(size=ne, tau=5.),
delay=0.1,
comm=bp.dnn.JitFPHomoLinear(ne, ni, prob=0.02, weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.I)
self.I2E = bp.dyn.ProjAlignPreMg1(pre=self.I,
syn=bp.dyn.Expon.desc(size=ni, tau=10.),
delay=0.1,
comm=bp.dnn.JitFPHomoLinear(ni, ne, prob=0.02, weight=6.7),
out=bp.dyn.COBA(E=-80.),
post=self.E)
self.I2I = bp.dyn.ProjAlignPreMg1(pre=self.I,
syn=bp.dyn.Expon.desc(size=ni, tau=10.),
delay=0.1,
comm=bp.dnn.JitFPHomoLinear(ni, ni, prob=0.02, weight=6.7),
out=bp.dyn.COBA(E=-80.),
post=self.I)

def update(self, inp):
self.E2E()
self.E2I()
self.I2E()
self.I2I()
self.E(inp)
self.I(inp)
return self.E.spike

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)


def model5():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
ne, ni = 3200, 800
self.E = bp.dyn.LifRefLTC(ne, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.I = bp.dyn.LifRefLTC(ni, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 2.))
self.E2E = bp.dyn.ProjAlignPreMg2(pre=self.E,
delay=0.1,
syn=bp.dyn.Expon.desc(size=ne, tau=5.),
comm=bp.dnn.JitFPHomoLinear(ne, ne, prob=0.02, weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.E)
self.E2I = bp.dyn.ProjAlignPreMg2(pre=self.E,
delay=0.1,
syn=bp.dyn.Expon.desc(size=ne, tau=5.),
comm=bp.dnn.JitFPHomoLinear(ne, ni, prob=0.02, weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.I)
self.I2E = bp.dyn.ProjAlignPreMg2(pre=self.I,
delay=0.1,
syn=bp.dyn.Expon.desc(size=ni, tau=10.),
comm=bp.dnn.JitFPHomoLinear(ni, ne, prob=0.02, weight=6.7),
out=bp.dyn.COBA(E=-80.),
post=self.E)
self.I2I = bp.dyn.ProjAlignPreMg2(pre=self.I,
delay=0.1,
syn=bp.dyn.Expon.desc(size=ni, tau=10.),
comm=bp.dnn.JitFPHomoLinear(ni, ni, prob=0.02, weight=6.7),
out=bp.dyn.COBA(E=-80.),
post=self.I)

def update(self, inp):
self.E2E()
self.E2I()
self.I2E()
self.I2I()
self.E(inp)
self.I(inp)
return self.E.spike

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)


def vanalla_proj():
class EINet(bp.DynSysGroup):
def __init__(self):
super().__init__()
self.N = bp.dyn.LifRefLTC(4000, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
V_initializer=bp.init.Normal(-55., 1.))
self.delay = bp.VarDelay(self.N.spike, entries={'delay': 2})
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.CSRLinear(bp.conn.FixedProb(0.02, pre=3200, post=4000), weight=0.6),
out=bp.dyn.COBA(E=0.),
post=self.N
)
self.I = bp.dyn.VanillaProj(
comm=bp.dnn.CSRLinear(bp.conn.FixedProb(0.02, pre=800, post=4000), 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(10000)
spks = bm.for_loop(lambda i: model.step_run(i, 20.), indices, progress_bar=True)
bp.visualize.raster_plot(indices, spks, show=True)


if __name__ == '__main__':
# model1()
# model2()
# model3()
# model4()
# model5()
vanalla_proj()

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