In BrainPy, each object (any instance of brainpy.DynamicalSystem) has the inherent monitor. Users can set up the monitor when initializing the brain objects. For example, if you have the following HH neuron model,

[1]:

import brainpy as bp

class HH(bp.NeuGroup):
target_backend = 'numpy'

def __init__(self, size, ENa=50., EK=-77., EL=-54.387,
C=1.0, gNa=120., gK=36., gL=0.03, V_th=20.,
**kwargs):
super(HH, self).__init__(size=size, **kwargs)

# parameters
self.ENa = ENa
self.EK = EK
self.EL = EL
self.C = C
self.gNa = gNa
self.gK = gK
self.gL = gL
self.V_th = V_th

# variables
self.V = bp.ops.ones(self.num) * -65.
self.m = bp.ops.ones(self.num) * 0.5
self.h = bp.ops.ones(self.num) * 0.6
self.n = bp.ops.ones(self.num) * 0.32
self.spike = bp.ops.zeros(self.num)
self.input = bp.ops.zeros(self.num)

@bp.odeint(method='exponential_euler')
def integral(self, V, m, h, n, t, Iext):
alpha = 0.1 * (V + 40) / (1 - bp.ops.exp(-(V + 40) / 10))
beta = 4.0 * bp.ops.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m

alpha = 0.07 * bp.ops.exp(-(V + 65) / 20.)
beta = 1 / (1 + bp.ops.exp(-(V + 35) / 10))
dhdt = alpha * (1 - h) - beta * h

alpha = 0.01 * (V + 55) / (1 - bp.ops.exp(-(V + 55) / 10))
beta = 0.125 * bp.ops.exp(-(V + 65) / 80)
dndt = alpha * (1 - n) - beta * n

I_Na = (self.gNa * m ** 3 * h) * (V - self.ENa)
I_K = (self.gK * n ** 4) * (V - self.EK)
I_leak = self.gL * (V - self.EL)
dVdt = (- I_Na - I_K - I_leak + Iext) / self.C

return dVdt, dmdt, dhdt, dndt

def update(self, _t, _i, _dt):
V, m, h, n = self.integral(self.V, self.m, self.h, self.n, _t, self.input)
self.spike = (self.V < self.V_th) * (V >= self.V_th)
self.V = V
self.m = m
self.h = h
self.n = n
self.input[:] = 0


the monitor can be set up when users create a HH neuron group:

[2]:

# set up a monitor using a list/tuple of strings
group1 = HH(size=10, monitors=['V', 'spike'])

type(group1.mon)

[2]:

brainpy.simulation.monitors.Monitor

[3]:

# set up a monitor using the Monitor class
group2 = HH(size=10, monitors=bp.simulation.Monitor(variables=['V', 'spike']))


Once we run the given model/network, the monitors will record the evolution of variables in the corresponding neural or synaptic models.

[4]:

group1.run(100., inputs=('input', 10))

bp.visualize.line_plot(group1.mon.V_t, group1.mon.V, show=True)


## Monitor variables at the selected index¶

However, we do not always take care of the all the content in a variable. We may be only interested in the values at the selected index. Moreover, for huge networks and long simulations, monitors will be a big part to consume RAM. Monitoring variables only at the selected index is a good solution. For these scenarios, we can initialize the monitors with the format of tuple/dict like this:

[5]:

group3 = HH(
size=10,
monitors=['V', ('spike', [1, 2, 3])]  # use a tuple to specify the (key, index)
)

group3.run(100., inputs=('input', 10.))

print(f'The monitor shape of "V" is (run length, variable size) = {group3.mon.V.shape}')
print(f'The monitor shape of "spike" is (run length, index size) = {group3.mon.spike.shape}')

The monitor shape of "V" is (run length, variable size) = (1000, 10)
The monitor shape of "spike" is (run length, index size) = (1000, 3)


Or, use a dictionary to specify the interested index of the variable:

[6]:

group4 = HH(
size=10,
monitors={'V': None, 'spike': [1, 2, 3]}  # use a dict to specify the {key: index}
)

group4.run(100., inputs=('input', 10.))

print(f'The monitor shape of "V" is (run length, variable size) = {group4.mon.V.shape}')
print(f'The monitor shape of "spike" is (run length, index size) = {group4.mon.spike.shape}')

The monitor shape of "V" is (run length, variable size) = (1000, 10)
The monitor shape of "spike" is (run length, index size) = (1000, 3)


Also, an instance of Monitor class can also be used:

[7]:

group5 = HH(
size=10,
# monitors=bp.simulation.Monitor(variables=['V', ('spike', [1, 2, 3])])
monitors=bp.simulation.Monitor(variables={'V': None, 'spike': [1, 2, 3]})  # initialize a Monitor
# to specify the key-index
)

group5.run(100., inputs=('input', 10.))

print(f'The monitor shape of "V" is (run length, variable size) = {group5.mon.V.shape}')
print(f'The monitor shape of "spike" is (run length, index size) = {group5.mon.spike.shape}')

The monitor shape of "V" is (run length, variable size) = (1000, 10)
The monitor shape of "spike" is (run length, index size) = (1000, 3)


## Monitor variables with customized period¶

In long simulations with small dt time step, what we take care about is the trend of the variable evolution, not the exact values at each time point (especially when dt is very small). For this scenario, we can initializing the monitors with the every item specification (similar to the decorator @brainpy.every(time=...)):

[8]:

group6 = HH(
size=10,
monitors=bp.simulation.Monitor(variables={'V': None, 'spike': [1, 2, 3]},
every={'V': None, 'spike': 1.})
)


In this example, we monitor “spike” variables at the index of [1, 2, 3] for each 1 ms.

[9]:

group6.run(100., inputs=('input', 10.))

print(f'The monitor shape of "V" = {group6.mon.V.shape}')
print(f'The monitor shape of "spike" = {group6.mon.spike.shape}')

The monitor shape of "V" = (1000, 10)
The monitor shape of "spike" = (100, 3)


But what is different from the decorator @brainpy.every(time=...) is that the time can not receive a bool function. This is because the monitors will allocate the data need to record in advance. But the bool function makes the beforehand allocation more difficult.

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