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"""
Check the speed of different Brian 2 configurations
"""
from brian2 import *
from brian2.tests.features import SpeedTest
__all__ = [
"LinearNeuronsOnly",
"HHNeuronsOnly",
"CUBAFixedConnectivity",
"COBAHHFixedConnectivity",
"VerySparseMediumRateSynapsesOnly",
"SparseMediumRateSynapsesOnly",
"DenseMediumRateSynapsesOnly",
"SparseLowRateSynapsesOnly",
"SparseHighRateSynapsesOnly",
"STDP",
]
class LinearNeuronsOnly(SpeedTest):
category = "Neurons only"
name = "Linear 1D"
tags = ["Neurons"]
n_range = [10, 100, 1000, 10000, 100000, 1000000]
n_label = "Num neurons"
# configuration options
duration = 10 * second
def run(self):
self.tau = tau = 1 * second
self.v_init = linspace(0.1, 1, self.n)
G = self.G = NeuronGroup(self.n, "dv/dt=-v/tau:1")
self.G.v = self.v_init
self.timed_run(self.duration)
class HHNeuronsOnly(SpeedTest):
category = "Neurons only"
name = "Hodgkin-Huxley"
tags = ["Neurons"]
n_range = [10, 100, 1000, 10000, 100000]
n_label = "Num neurons"
# configuration options
duration = 1 * second
def run(self):
num_neurons = self.n
# Parameters
area = 20000 * umetre**2
Cm = 1 * ufarad * cm**-2 * area
gl = 5e-5 * siemens * cm**-2 * area
El = -65 * mV
EK = -90 * mV
ENa = 50 * mV
g_na = 100 * msiemens * cm**-2 * area
g_kd = 30 * msiemens * cm**-2 * area
VT = -63 * mV
# The model
eqs = Equations(
"""
dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
(exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
(exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
(exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
I : amp
"""
)
# Threshold and refractoriness are only used for spike counting
group = NeuronGroup(
num_neurons, eqs, threshold="v > -40*mV", refractory="v > -40*mV"
)
group.v = El
group.I = "0.7*nA * i / num_neurons"
self.timed_run(self.duration)
class CUBAFixedConnectivity(SpeedTest):
category = "Full examples"
name = "CUBA fixed connectivity"
tags = ["Neurons", "Synapses", "SpikeMonitor"]
n_range = [10, 100, 1000, 10000, 100000]
n_label = "Num neurons"
# configuration options
duration = 1 * second
def run(self):
N = self.n
Ne = int(0.8 * N)
taum = 20 * ms
taue = 5 * ms
taui = 10 * ms
Vt = -50 * mV
Vr = -60 * mV
El = -49 * mV
eqs = """
dv/dt = (ge+gi-(v-El))/taum : volt (unless refractory)
dge/dt = -ge/taue : volt (unless refractory)
dgi/dt = -gi/taui : volt (unless refractory)
"""
P = NeuronGroup(N, eqs, threshold="v>Vt", reset="v = Vr", refractory=5 * ms)
P.v = "Vr + rand() * (Vt - Vr)"
P.ge = 0 * mV
P.gi = 0 * mV
we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage)
wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight
Ce = Synapses(P, P, on_pre="ge += we")
Ci = Synapses(P, P, on_pre="gi += wi")
Ce.connect("i<Ne", p=80.0 / N)
Ci.connect("i>=Ne", p=80.0 / N)
s_mon = SpikeMonitor(P)
self.timed_run(self.duration)
class COBAHHFixedConnectivity(SpeedTest):
category = "Full examples"
name = "COBAHH fixed connectivity"
tags = ["Neurons", "Synapses", "SpikeMonitor"]
n_range = [100, 500, 1000, 5000, 10000, 50000, 100000, 500000, 1000000]
n_label = "Num neurons"
# configuration options
duration = 1 * second
def run(self):
N = self.n
area = 20000 * umetre**2
Cm = (1 * ufarad * cm**-2) * area
gl = (5e-5 * siemens * cm**-2) * area
El = -60 * mV
EK = -90 * mV
ENa = 50 * mV
g_na = (100 * msiemens * cm**-2) * area
g_kd = (30 * msiemens * cm**-2) * area
VT = -63 * mV
# Time constants
taue = 5 * ms
taui = 10 * ms
# Reversal potentials
Ee = 0 * mV
Ei = -80 * mV
we = 6 * nS # excitatory synaptic weight
wi = 67 * nS # inhibitory synaptic weight
# The model
eqs = Equations(
"""
dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-
g_na*(m*m*m)*h*(v-ENa)-
g_kd*(n*n*n*n)*(v-EK))/Cm : volt
dm/dt = alpha_m*(1-m)-beta_m*m : 1
dn/dt = alpha_n*(1-n)-beta_n*n : 1
dh/dt = alpha_h*(1-h)-beta_h*h : 1
dge/dt = -ge*(1./taue) : siemens
dgi/dt = -gi*(1./taui) : siemens
alpha_m = 0.32*(mV**-1)*(13*mV-v+VT)/
(exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz
beta_m = 0.28*(mV**-1)*(v-VT-40*mV)/
(exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz
alpha_h = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz
beta_h = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz
alpha_n = 0.032*(mV**-1)*(15*mV-v+VT)/
(exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz
beta_n = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz
"""
)
P = NeuronGroup(
N,
model=eqs,
threshold="v>-20*mV",
refractory=3 * ms,
method="exponential_euler",
)
P.v = "El + (randn() * 5 - 5)*mV"
P.ge = "(randn() * 1.5 + 4) * 10.*nS"
P.gi = "(randn() * 12 + 20) * 10.*nS"
Pe = P[: int(0.8 * N)]
Pi = P[int(0.8 * N) :]
Ce = Synapses(Pe, P, on_pre="ge+=we")
Ci = Synapses(Pi, P, on_pre="gi+=wi")
Ce.connect(p=80.0 / N)
Ci.connect(p=80.0 / N)
s_mon = SpikeMonitor(P)
self.timed_run(self.duration)
class STDP(SpeedTest):
category = "Full examples"
name = "STDP with Poisson input"
tags = ["Neurons", "Synapses", "SpikeMonitor", "PoissonGroup"]
n_range = [100, 500, 1000, 5000, 10000, 50000, 100000, 500000, 1000000]
n_label = "Num neurons"
# configuration options
duration = 1 * second
def run(self):
N = self.n
taum = 10 * ms
taupre = 20 * ms
taupost = taupre
Ee = 0 * mV
vt = -54 * mV
vr = -60 * mV
El = -74 * mV
taue = 5 * ms
F = 15 * Hz
gmax = 0.01
dApre = 0.01
dApost = -dApre * taupre / taupost * 1.05
dApost *= gmax
dApre *= gmax
eqs_neurons = """
dv/dt = (ge * (Ee-vr) + El - v) / taum : volt
dge/dt = -ge / taue : 1
"""
poisson_input = PoissonGroup(N, rates=F)
neurons = NeuronGroup(
1, eqs_neurons, threshold="v>vt", reset="v = vr", method="exact"
)
S = Synapses(
poisson_input,
neurons,
"""
w : 1
dApre/dt = -Apre / taupre : 1 (event-driven)
dApost/dt = -Apost / taupost : 1 (event-driven)
""",
on_pre="""ge += w
Apre += dApre
w = clip(w + Apost, 0, gmax)""",
on_post="""Apost += dApost
w = clip(w + Apre, 0, gmax)""",
)
S.connect()
S.w = "rand() * gmax"
s_mon = SpikeMonitor(poisson_input)
self.timed_run(self.duration)
class SynapsesOnly:
category = "Synapses only"
tags = ["Synapses"]
n_range = [10, 100, 1000, 10000]
n_label = "Num neurons"
duration = 1 * second
# memory usage will be approximately p**2*rate*dt*N**2*bytes_per_synapse/1024**3 GB
# for CPU, bytes_per_synapse appears to be around 40?
def run(self):
N = self.n
rate = self.rate
M = int(rate * N * defaultclock.dt)
if M <= 0:
M = 1
G = NeuronGroup(M, "v:1", threshold="True")
H = NeuronGroup(N, "w:1")
S = Synapses(G, H, on_pre="w += 1.0")
S.connect(True, p=self.p)
# M = SpikeMonitor(G)
self.timed_run(
self.duration,
# report='text',
)
# plot(M.t/ms, M.i, ',k')
class VerySparseMediumRateSynapsesOnly(SynapsesOnly, SpeedTest):
name = "Very sparse, medium rate (10s duration)"
rate = 10 * Hz
p = 0.02
n_range = [10, 100, 1000, 10000, 100000]
duration = 10 * second
class SparseMediumRateSynapsesOnly(SynapsesOnly, SpeedTest):
name = "Sparse, medium rate (1s duration)"
rate = 10 * Hz
p = 0.2
n_range = [10, 100, 1000, 10000, 100000]
class DenseMediumRateSynapsesOnly(SynapsesOnly, SpeedTest):
name = "Dense, medium rate (1s duration)"
rate = 10 * Hz
p = 1.0
n_range = [10, 100, 1000, 10000, 40000]
class SparseLowRateSynapsesOnly(SynapsesOnly, SpeedTest):
name = "Sparse, low rate (10s duration)"
rate = 1 * Hz
p = 0.2
n_range = [10, 100, 1000, 10000, 100000]
duration = 10 * second
class SparseHighRateSynapsesOnly(SynapsesOnly, SpeedTest):
name = "Sparse, high rate (1s duration)"
rate = 100 * Hz
p = 0.2
n_range = [10, 100, 1000, 10000]
if __name__ == "__main__":
# prefs.codegen.target = 'numpy'
VerySparseMediumRateSynapsesOnly(100000).run()
show()
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