# Monitors and Inputs

BrainPy has a systematic naming system. Any model in BrainPy have a unique name. Thus, nodes, integrators, and variables can be easily accessed in a huge network. Based on this naming system, BrainPy provides a set of convenient monitoring and input supports. In this tutorial, we are going to talk about this.

import brainpy as bp
import brainpy.math as bm

bp.math.set_platform('cpu')
bp.math.set_dt(0.02)

import numpy as np
import matplotlib.pyplot as plt


## Monitors

In BrainPy, any instance of brainpy.Runner has a build-in monitor. Users can set up the monitor when initializing a runner.

Here, we have the following HH neuron model,

class HH(bp.NeuGroup):
def __init__(self, size, ENa=50., EK=-77., EL=-54.387, C=1.0,
gNa=120., gK=36., gL=0.03, V_th=20., name=None):
super(HH, self).__init__(size=size, name=name)

# 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 = bm.Variable(bm.ones(self.num) * -65.)
self.m = bm.Variable(bm.ones(self.num) * 0.5)
self.h = bm.Variable(bm.ones(self.num) * 0.6)
self.n = bm.Variable(bm.ones(self.num) * 0.32)
self.input = bm.Variable(bm.zeros(self.num))
self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))

# functions
self.integral = bp.odeint(self.derivative, method='rk4')

def derivative(self, V, m, h, n, t, Iext):
alpha = 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))
beta = 4.0 * bm.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m

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

alpha = 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))
beta = 0.125 * bm.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, _dt):
V, m, h, n = self.integral(self.V, self.m, self.h, self.n, _t, self.input)
self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)
self.V.value = V
self.m.value = m
self.h.value = h
self.n.value = n
self.input[:] = 0

model  = HH(1)


First method is to initialize a monitor is using a list/tuple of strings.

# set up a monitor using a list of str
runner1 = bp.StructRunner(model,
monitors=['V', 'spike'],
inputs=('input', 10))

type(runner1.mon)

brainpy.simulation.monitor.Monitor


The initialized monitor is an instance of brainpy.Monitor. Therefore, users can also directly use Monitor class to initialize a monitor.

# set up a monitor using brainpy.Monitor
runner2 = bp.StructRunner(model, monitors=bp.Monitor(variables=['V', 'spike']))


Once we call the .run() function in the runner, the monitor will automatically record the variable evolutions in the corresponding models. Afterwards, users can access these variable trajectories by using .mon.[variable_name]. The default history times .mon.ts will also be generated after the model finishes its running. Let’s see an example.

runner1.run(100.)

bp.visualize.line_plot(runner1.mon.ts, runner1.mon.V, show=True)


The monitor in runner1 has recorded the evolution of V. Therefore, it can be accessed by runner1.mon.V or equivalently runner1.mon['V']. Similarly, the recorded trajectory of variable spike can also be obtained through runner1.mon.spike.

runner1.mon.spike

array([[False],
[False],
[False],
...,
[False],
[False],
[False]])


### The mechanism of monitors

We want to record HH.V and HH.spike, why we define monitors=['V', 'spike'] during HH initialization is successful? How does brainpy.Monitor recognize what variables I want to trace?

Actually, BrainPy first tries to find the interested variables in the simulation target by the relative path. If not found, BrainPy checks whether the variable can be accessed by the absolute path by the simluation target. Not found again? An error will be raised.

net = bp.Network(HH(size=10, name='X'),
HH(size=20, name='Y'),
HH(size=30))

# it's ok
bp.StructRunner(net, monitors=['X.V', 'Y.spike']).build_monitors()

<function monitor_step(_t, _dt)>


In the above net, there are HH instances named as “X” and “Y”. Therefore, trying to monitor “X.V” and “Y.spike” is successful.

However, in the following example, node named with “Z” is not accessible in the generated net. Therefore the monitoring setup failed.

z = HH(size=30, name='Z')
net = bp.Network(HH(size=10), HH(size=20))

# node "Z" can not be accessed in the simulation target 'net'
try:
bp.StructRunner(net, monitors=['Z.V']).build_monitors()
except Exception as e:
print(type(e).__name__, ":", e)

RunningError : Cannot find target Z.V in monitor of <brainpy.simulation.brainobjects.network.Network object at 0x000002A3D28D19A0>, please check.


Note

BrainPy only supports to monitor Variable. This is because monitoring Variable’s trajectory is meaningful. They are dynamically changed, and others not marked as Variable will be compiled as constants.

try:
bp.StructRunner(HH(size=1), monitors=['gNa']).build_monitors()
except Exception as e:
print(type(e).__name__, ":", e)

RunningError : "gNa" in <__main__.HH object at 0x000002A3D239ADF0> is not a dynamically changed Variable, its value will not change, we think there is no need to monitor its trajectory.


Note

The monitors in BrainPy only record the flattened tensor values. This means if your target variable is a matrix with the shape of (N, M), the resulting trajectory value in the monitor after running T times will be a tensor with the shape of (T, N x M).

class MatrixVarModel(bp.DynamicalSystem):
def __init__(self, **kwargs):
super(MatrixVarModel, self).__init__(**kwargs)

self.a = bm.Variable(bm.zeros((4, 4)))

def update(self, _t, _dt):
self.a += 0.01

model = MatrixVarModel()
duration = 10
runner = bp.StructRunner(model, monitors=['a'])
runner.run(duration)

print(f'The expected shape of "model.mon.a" is: {(int(duration/bm.get_dt()), model.a.size)}')
print(f'The actual shape of "model.mon.a" is: {runner.mon.a.shape}')

The expected shape of "model.mon.a" is: (500, 16)
The actual shape of "model.mon.a" is: (500, 16)


### Monitor variables at the selected index

Sometimes, 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 a huge network with a long time simulation, monitors will be a big part to consume RAM. So, only monitoring variables at the selected index will be a good solution. Fortunately, BrainPy supports to monitor a part of elements in a Variable with the format of tuple/dict like this:

runner = bp.StructRunner(
HH(10),
monitors=['V',  # monitor all values of Variable 'V'
('spike', [1, 2, 3])], # monitor values of Variable at index of [1, 2, 3]
inputs=('input', 10.)
)
runner.run(100.)

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

The monitor shape of "V" is (run length, variable size) = (5000, 10)
The monitor shape of "spike" is (run length, index size) = (5000, 3)


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

runner = bp.StructRunner(
HH(10),
monitors={'V': None,  # 'None' means all values will be monitored
'spike': [1, 2, 3]},  # specify the interested index
inputs=('input', 10.),
)
runner.run(100.)

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

The monitor shape of "V" is (run length, variable size) = (5000, 10)
The monitor shape of "spike" is (run length, index size) = (5000, 3)


Also, we can directly instantiate a brainpy.Monitor class:

runner = bp.StructRunner(
HH(10),
monitors=bp.Monitor(variables=['V', ('spike', [1, 2, 3])]),
inputs=('input', 10.),
)
runner.run(100.)

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

The monitor shape of "V" is (run length, variable size) = (5000, 10)
The monitor shape of "spike" is (run length, index size) = (5000, 3)

runner = bp.StructRunner(
HH(10),
monitors=bp.Monitor(variables={'V': None, 'spike': [1, 2, 3]}),
inputs=('input', 10.),
)
runner.run(100.)

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

The monitor shape of "V" is (run length, variable size) = (5000, 10)
The monitor shape of "spike" is (run length, index size) = (5000, 3)


Note

When users want to record a small part of a variable whose dimension > 1, due to brainpy.Monitor records a flattened tensor variable, they must provide the index positions at the flattened tensor.

### Monitor variables with a customized period

In a long simulation with a small time step dt , 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 initialize the monitors with the intervals item specification.

However, intervals setting is only supported in the brainpy.ReportRunner.

runner = bp.ReportRunner(
HH(10),
monitors=bp.Monitor(variables={'V': None, 'spike': [1, 2, 3]},
intervals={'V': None, 'spike': 1.}),  # in 1 ms, we record 'spike' only once
inputs=('input', 10.),
)


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

runner.run(100.)

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

Compilation used 0.2560 s.
Start running ...
Run 10.0% used 3.361 s.
Run 20.0% used 6.620 s.
Run 30.0% used 9.808 s.
Run 40.0% used 12.977 s.
Run 50.0% used 16.157 s.
Run 60.0% used 19.337 s.
Run 70.0% used 22.539 s.
Run 80.0% used 25.746 s.
Run 90.0% used 28.946 s.
Run 100.0% used 32.139 s.
Simulation is done in 32.139 s.

The monitor shape of "V" = (5000, 10)
The monitor shape of "spike" = (99, 3)


It’s worthy to note that for the monitor variable [variable_name] with a non-none intervals specification, a corresponding time item [variable_name].t will be generated in the monitor. This is because it’s time trajectory will be different from the default time trajectory.

print('The shape of ["spike"]: ', runner.mon['spike'].shape)
print('The shape of ["spike.t"]: ', runner.mon['spike.t'].shape)

print('group7.mon["spike.t"]: ', runner.mon["spike.t"])

The shape of ["spike"]:  (99, 3)
The shape of ["spike.t"]:  (99,)
group7.mon["spike.t"]:  [ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.
37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54.
55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72.
73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90.
91. 92. 93. 94. 95. 96. 97. 98. 99.]


## Inputs

BrainPy also provides inputs operation for each instance of brainpy.Runner.

The aim of inputs is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording. inputs should have the format like (target, value, [type, operation]), where

• target is the target variable to inject the input.

• value is the input value. It can be a scalar, a tensor, or a iterable object/function.

• type is the type of the input value. It support two types of input: fix and iter.

• operation is the input operation on the target variable. It should be set as one of { + , - , * , / , = }, and if users do not provide this item explicitly, it will be set to ‘+’ by default, which means that the target variable will be updated as val = val + input.

You can also give multiple inputs for different target variables, like:


inputs=[(target1, value1, [type1, op1]),
(target2, value2, [type2, op2]),
... ]


### The mechanism of inputs

The mechanism of inputs is the same with monitors. BrainPy finds out the target variables to do the input operation through the absolute or relative path.

class Model(bp.DynamicalSystem):
def __init__(self, num_sizes, **kwargs):
super(Model, self).__init__(**kwargs)

self.l1 = HH(num_sizes[0], name='L')
self.l2 = HH(num_sizes[1])
self.l3 = HH(num_sizes[2])

def update(self, _t, _dt):
self.l1.update(_t, _dt)
self.l2.update(_t, _dt)
self.l3.update(_t, _dt)

model = Model([10, 20, 30])

runner = bp.StructRunner(
model,
inputs=[('L.V', 2.0),  # access with the absolute path
('l2.V', 1),]  # access with the relative path
)
runner.run(100.)

2.9371795654296875


inputs supports two types of data: fix and iter. The first one means that the data is static; the second one denotes the data can be iterable, no matter the input value is a tensor or a function. Note, 'iter' type must be explicitly stated.

# a tensor

runner = bp.StructRunner(model, inputs=('L.V', bm.ones(1000) * 2., 'iter'))
runner.run(100.)

2.8953235149383545

# a function

def current():
while True: yield 2.

runner = bp.StructRunner(model, inputs=('L.V', current(), 'iter'))
runner.run(100.)

2.9955742359161377


## Input construction functions

Inputs are common in a computational experiment. Also, we need various kind of inputs. In BrainPy, we provide several convenient input functions to help users construct input currents.

### brainpy.inputs.section_input()

brainpy.inputs.section_input() is an updated function of previous brainpy.inputs.constant_input() (see below).

Sometimes, we need input currents with different values in different periods. For example, if you want to get an input in which 0-100 ms is zero, 100-400 ms is value 1., and 400-500 ms is zero, then, you can define:

current, duration = bp.inputs.section_input(values=[0, 1., 0.],
durations=[100, 300, 100],
return_length=True)

def show(current, duration, title):
ts = np.arange(0, duration, 0.1)
plt.plot(ts, current)
plt.title(title)
plt.xlabel('Time [ms]')
plt.ylabel('Current Value')
plt.show()

show(current, duration, 'values=[0, 1, 0], durations=[100, 300, 100]')


### brainpy.inputs.constant_input()

brainpy.inputs.constant_input() function helps you to format constant currents in several periods.

For the input created above, we can define it again with constant_input() by:

current, duration = bp.inputs.constant_input([(0, 100), (1, 300), (0, 100)])

show(current, duration, '[(0, 100), (1, 300), (0, 100)]')


Another example is this:

current, duration = bp.inputs.constant_input([(-1, 10), (1, 3), (3, 30), (-0.5, 10)], dt=0.1)

show(current, duration, '[(-1, 10), (1, 3), (3, 30), (-0.5, 10)]')


### brainpy.inputs.spike_input()

brainpy.inputs.spike_input() helps you to construct an input like a series of short-time spikes. It receives the following settings:

• sp_times : The spike time-points. Must be an iterable object. For example, list, tuple, or arrays.

• sp_lens : The length of each point-current, mimicking the spike durations. It can be a scalar float to specify the unified duration. Or, it can be list/tuple/array of time lengths with the length same with sp_times.

• sp_sizes : The current sizes. It can be a scalar value. Or, it can be a list/tuple/array of spike current sizes with the length same with sp_times.

• duration : The total current duration.

• dt : The time step precision. The default is None (will be initialized as the default dt step).

For example, if you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, and each spike lasts 1 ms and the spike current is 0.5, then you can use the following funtions:

current = bp.inputs.spike_input(
sp_times=[10, 20, 30, 200, 300],
sp_lens=1.,  # can be a list to specify the spike length at each point
sp_sizes=0.5,  # can be a list to specify the spike current size at each point
duration=400.)

show(current, 400, 'Spike Input Example')


### brainpy.inputs.ramp_input()

brainpy.inputs.ramp_input() mimics a ramp or a step current to the input of the circuit. It receives the following settings:

• c_start : The minimum (or maximum) current size.

• c_end : The maximum (or minimum) current size.

• duration : The total duration.

• t_start : The ramped current start time-point.

• t_end : The ramped current end time-point. Default is the None.

• dt : The current precision.

We illustrate the usage of brainpy.inputs.ramp_input() by two examples.

In the first example, we increase the current size from 0. to 1. between the start time (0 ms) and the end time (1000 ms).

duration = 1000
current = bp.inputs.ramp_input(0, 1, duration)

show(current, duration, r'$c_{start}$=0, $c_{end}$=%d, duration, '
r'$t_{start}$=0, $t_{end}$=None' % (duration))


In the second example, we increase the current size from 0. to 1. from the 200 ms to 800 ms.

duration, t_start, t_end = 1000, 200, 800
current = bp.inputs.ramp_input(0, 1, duration, t_start, t_end)

show(current, duration, r'$c_{start}$=0, $c_{end}$=1, duration=%d, '
r'$t_{start}$=%d, $t_{end}$=%d' % (duration, t_start, t_end))


### General property of input functions

There are several general properties for input construction functions.

Property 1: All input functions can automatically broadcast the current shapes, if they are heterogenous among different periods. For example, during period 1 we give an input with a scalar value, during period 2 we give an input with a vector shape, and during period 3 we give a matrix input value. Input functions will broadcast them to the maximum shape. For example,

current = bp.inputs.section_input(values=[0, bm.ones(10), bm.random.random((3, 10))],
durations=[100, 300, 100])

current.shape

(5000, 3, 10)


Property 2: Every input function receives a dt specification. If dt is not provided, input functions will use the default dt in the whole BrainPy system.

bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.02).shape

(3000,)

bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.2).shape

(300,)

# the default 'dt' in 0.1

bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30]).shape

(600,)