# 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 section, we are going to talk about this.

import brainpy as bp
import brainpy.math as bm

import numpy as np
import matplotlib.pyplot as plt


## Monitors

In BrainPy, any instance of brainpy.DynamicalSystem has a build-in monitor. Users can set up the monitor when initializing the brain object. For example, if you 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., **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 = 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='exponential_euler')

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[:] = bm.logical_and(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.

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

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

type(group1.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
group2 = HH(size=10, monitors=bp.Monitor(variables=['V', 'spike']))


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

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

bp.visualize.line_plot(group1.mon.ts, group1.mon.V, show=True) The monitor in group1 has recorded the evolution of V. Therefore, it can be accessed by group1.mon.V or equivalently group1.mon['V']. Similarly, the recorded trajectory of variable spike can also be obtained through group1.mon.spike.

group1.mon.spike

array([[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[ True,  True,  True, ...,  True,  True,  True],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[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, given the monitor targets, BrainPy, first of all, check whether this key is the attribute of the node which defines this monitor key. For monitor targets 'V' and 'spike', it is really the attributes of HH model. However, if not, BrainPy first check whether the key’s host (brainpy.DynamicalSystem class object) can be accessible in .nodes(), then check whether the host has the specified variable. For example, we define a network, and define the monitor target by the absolute path.

net = bp.Network(HH(size=10, name='X'),
HH(size=20, name='Y'),
HH(size=30),
monitors=['X.V', 'Y.spike'])

net.build()  # it's ok


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), monitors=['Z.V'])

# node "Z" can not be accessed in 'net.nodes()'
try:
net.build()
except Exception as e:
print(type(e).__name__, ":", e)

BrainPyError : Cannot find target Z.V in monitor of <brainpy.simulation.brainobjects.network.Network object at 0x7fc14c454610>, please check.


Note

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

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

BrainPyError : "gNa" in <__main__.HH object at 0x7fc14c3d1f40> is not a dynamically changed Variable, its value will not change, we cannot 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

duration = 10
model = MatrixVarModel(monitors=['a'])
model.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: {model.mon.a.shape}')

The expected shape of "model.mon.a" is: (100, 16)
The actual shape of "model.mon.a" is: (100, 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:

group3 = HH(
size=10,
monitors=[
'V',  # monitor all values of Variable 'V'
('spike', [1, 2, 3]), # monitor values of Variable at index of [1, 2, 3]
]
)

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

group4 = HH(
size=10,
monitors={'V': None,  # 'None' means all values will be monitored
'spike': [1, 2, 3]}  # specify the interested 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, we can directly instantiate brainpy.Monitor class:

group5 = HH(
size=10,
monitors=bp.Monitor(variables=['V', ('spike', [1, 2, 3])])
)
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)

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


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:

group7 = HH(
size=10,
monitors=bp.Monitor(variables={'V': None, 'spike': [1, 2, 3]},
intervals={'V': None, 'spike': 1.})  # in 1 ms, we record 'spike' only once
)

# The above instantiation is equivalent to:
#
# group7 = HH(
#   size=10, monitors=bp.Monitor(variables=['V', ('spike', [1, 2, 3])],
#                                intervals=[None, 1.])
# )


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

group7.run(100., inputs=('input', 10.))

print(f'The monitor shape of "V" = {group7.mon.V.shape}')
print(f'The monitor shape of "spike" = {group7.mon.spike.shape}')

The monitor shape of "V" = (1000, 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"]: ', group7.mon['spike'].shape)
print('The shape of ["spike.t"]: ', group7.mon['spike.t'].shape)

print('group7.mon["spike.t"]: ', group7.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.DynamicalSystem. It should be carried out during calling the .run(..., inputs=xxx) function.

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 (see The mechanism of monitors). BrainPy first check whether user specified target can be accessed by the relative path.

If not, BrainPy separate the host name and the variable name, and further check whether the host name is defined in the .node() and whether the variable name can be accessed by the retrieved host. Therefore, in a input setting, the target can be set with the absolute or relative path. For example, in the below network model,

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

self.l1 = HH(num_sizes, name='L')
self.l2 = HH(num_sizes)
self.l3 = HH(num_sizes)

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])

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

0.7689826488494873


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

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

0.7576150894165039

# a function

def current():
while True: yield 2.

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

0.767667293548584


### Current 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.

#### 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]') #### 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)]') #### 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') #### 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 current 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,)