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| import brainpy as bp | ||
| import brainpy.math as bm | ||
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| 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) | ||
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| 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 | ||
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| 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) | ||
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| 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) | ||
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| def update(self, inp): | ||
| self.E2E() | ||
| self.E2I() | ||
| self.I2E() | ||
| self.I2I() | ||
| self.E(inp) | ||
| self.I(inp) | ||
| return self.E.spike | ||
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| 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) | ||
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| 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) | ||
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| 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 | ||
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| 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) | ||
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| 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) | ||
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| def update(self, inp): | ||
| self.E2E() | ||
| self.E2I() | ||
| self.I2E() | ||
| self.I2I() | ||
| self.E(inp) | ||
| self.I(inp) | ||
| return self.E.spike | ||
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| 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) | ||
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| 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) | ||
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| def update(self, inp): | ||
| self.E2E() | ||
| self.E2I() | ||
| self.I2E() | ||
| self.I2I() | ||
| self.E(inp) | ||
| self.I(inp) | ||
| return self.E.spike | ||
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| 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) | ||
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| 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 | ||
| ) | ||
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| 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 | ||
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| 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) | ||
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| if __name__ == '__main__': | ||
| # model1() | ||
| # model2() | ||
| # model3() | ||
| # model4() | ||
| # model5() | ||
| vanalla_proj() |