Clock driven: Neurons

Author: fangwei123456

Translator: YeYumin

This tutorial focuses on spikingjelly.clock_driven.neuron and introduces spiking neurons and clock-driven simulation methods.

Spiking Nneuron Model

In spikingjelly, we define the neuron which can only output spikes, i.e. 0 or 1, as a “spiking neuron”. Networks that use spiking neurons are called Spiking Neural Networks (SNNs). spikingjelly.clock_driven.neuron defines various common spiking neuron models. We take spikingjelly.clock_driven.neuron.LIFNode as an example to introduce spiking neurons.

First, we need to import the relevant modules:

import torch
import torch.nn as nn
import numpy as np
from spikingjelly.clock_driven import neuron
from spikingjelly import visualizing
from matplotlib import pyplot as plt

And then we create a new LIF neurons layer:

lif = neuron.LIFNode()

The LIF neurons layer has some parameters, which are explained in detail in the API documentation:

  • tau – membrane time constant

  • v_threshold – the threshold voltage of the neuron

  • v_reset – the reset voltage of the neuron. If it is not None, when the neuron releases a spike, the voltage will be reset to v_reset; if it is set to None, the voltage will be subtracted from v_threshold

  • surrogate_function – the surrogate function used to calculate the gradient of the spike function during back propagation

The surrogate_function behaves exactly the same as the step function during forward propagation, and we will introduce its working principle for back propagation later. We can just ignore it now.

You may be curious about the number of neurons in this layer. For most neurons layers in spikingjelly.clock_driven.neuron, the number of neurons is automatically determined according to the shape of the received input after initialization or re-initialization by calling the reset() function.

Similar to neurons in RNN, spiking neurons are also stateful (they have memory). The state variable of a spiking neuron is generally its membrane potential \(V_{t}\). Therefore, neurons in spikingjelly.clock_driven.neuron have state variable v. We can print the membrane potential of the newly created LIF neurons layer:

# 0.0

We can find that lif.v is now 0.0 because we haven’t given it any input yet. We give several different inputs and observe the shape of lif.v. We can find that it is consistent with the numel of inputs:

x = torch.rand(size=[2, 3])
print('x.shape', x.shape, 'lif.v.shape', lif.v.shape)
# x.shape torch.Size([2, 3]) lif.v.shape torch.Size([2, 3])

x = torch.rand(size=[4, 5, 6])
print('x.shape', x.shape, 'lif.v.shape', lif.v.shape)
# x.shape torch.Size([4, 5, 6]) lif.v.shape torch.Size([4, 5, 6])

What is the relationship between \(V_{t}\) and input \(X_{t}\)? In the spiking neuron, it not only depends on the input \(X_{t}\) at time-step t, but also on its membrane potential \(V_{t-1}\) at the last time-step t-1.

We often use the sub-threshold (when the membrane potential does not exceed the threshold potential V_{threshold}) neuronal dynamics equation \(\frac{\mathrm{d}V(t)}{\mathrm{d}t} = f(V(t), X(t))\) to describe the continuous-time spiking neuron. For example. For LIF neurons, the equation is:

\[\tau_{m} \frac{\mathrm{d}V(t)}{\mathrm{d}t} = -(V(t) - V_{reset}) + X(t)\]

where \(\tau_{m}\) is the membrane time constant and \(V_{reset}\) is the reset potential. For such a differential equation, \(X(t)\) is not a constant and it is difficult to obtain a explicit analytical solution.

The neurons in spikingjelly.clock_driven.neuron use discrete difference equations to approximate continuous differential equations. From the perspective of the discrete equation, the charging equation of the LIF neuron is:

\[\tau_{m} (V_{t} - V_{t-1}) = -(V_{t-1} - V_{reset}) + X_{t}\]

The expression of \(V_{t}\) can be obtained as

\[V_{t} = f(V_{t-1}, X_{t}) = V_{t-1} + \frac{1}{\tau_{m}}(-(V_{t - 1} - V_{reset}) + X_{t})\]

The corresponding code can be found in spikingjelly.clock_driven.neuron.LIFNode.neuronal_charge:

def neuronal_charge(self, dv: torch.Tensor):
    if self.v_reset is None:
        self.v += (x - self.v) / self.tau

        if isinstance(self.v_reset, float) and self.v_reset == 0.:
            self.v += (x - self.v) / self.tau
            self.v += (x - (self.v - self.v_reset)) / self.tau

Different neurons have different charging equations. However, when the membrane potential exceeds the threshold potential, the release of spike and the reset of the membrane potential are the same for all kinds of neurons. Therefore, they all inherit from spikingjelly.clock_driven.neuron.BaseNode and share the same discharge and reset equations. The codes of neuronal fire can be found at spikingjelly.clock_driven.neuron.BaseNode.neuronal_fire:

def neuronal_fire(self):
    self.spike = self.surrogate_function(self.v - self.v_threshold)

surrogate_function() is a heaviside step function during forward propagation. When input is greater than or equal to 0, it will return 1, otherwise it will return 0. We regard this kind of tensor whose elements are only 0 or 1 as spikes.

The release of spikes consumes the previously accumulated electric charge of the neuron, so there will be an instantaneous decrease in the membrane potential, which is the neuronal reset. In SNNs, there are two ways to realize neuronal reset:

  1. Hard method: After releasing a spike, the membrane potential is directly set to the reset potential \(V = V_{reset}\)

  2. Soft method: After releasing a spike, the membrane potential subtracts the threshold voltage \(V = V - V_{threshold}\)

It can be found that for neurons using the soft method, there is no need to reset the voltage \(V_{reset}\). For the neurons in spikingjelly.clock_driven.neuron, when v_reset is set to the a float value (e.g., the default value is 1.0), the neuron uses the hard reset; if v_reset is set to None, the soft reset will be used. We can find the corresponding codes in spikingjelly.clock_driven.neuron.BaseNode.neuronal_fire.neuronal_reset:

def neuronal_reset(self):
    # ...
    if self.v_reset is None:
        self.v = self.v - spike * self.v_threshold
        self.v = (1 - spike) * self.v + spike * self.v_reset

Three Equations to Describe Discrete Spiking Neurons

We can use the three discrete equations: neuronal charge, neuronal fire, and neuronal reset to describe all kinds of discrete spiking neurons. The neuronal charge and fire equations are:

\[\begin{split}H_{t} & = f(V_{t-1}, X_{t}) \\ S_{t} & = g(H_{t} - V_{threshold}) = \Theta(H_{t} - V_{threshold})\end{split}\]

where \(\Theta(x)\) is the surrogate_function() in the parameters, which is a heaviside step function:

\[\begin{split}\Theta(x) = \begin{cases} 1, & x \geq 0 \\ 0, & x < 0 \end{cases}\end{split}\]

The hard reset is:

\[V_{t} = H_{t} \cdot (1 - S_{t}) + V_{reset} \cdot S_{t}\]

The soft reset is:

\[V_{t} = H_{t} - V_{threshold} \cdot S_{t}\]

where \(V_{t}\) is the membrane potential of the neuron, \(X_{t}\) is the external input, such as voltage increment. To avoid confusion, we use \(H_{t}\) to represent the membrane potential after neuronal charge but before neuronal fire, \(V_{t}\) is the membrane potential after the neuronal fire, \(f(V(t-1), X(t))\) is the neuronal charge function. The difference between neurons is the neuronal charge.

Clock-driven Simulation

spikingjelly.clock_driven uses a clock-driven approach to simulate SNN.

Next, we will stimulate the neuron and check its membrane potential and output spikes.

Now let us give constant input to the LIF neurons layer and plot the membrane potential and output spikes:

x = torch.as_tensor([2.])
T = 150
s_list = []
v_list = []
for t in range(T):

visualizing.plot_one_neuron_v_s(np.asarray(v_list), np.asarray(s_list), v_threshold=lif.v_threshold, v_reset=lif.v_reset,

The input is with shape=[1], and this LIF neurons layer has only 1 neuron. Its membrane potential and output spikes change with time-step as follows:


We reset the neurons layer and give an input with shape=[32] to see the membrane potential and output spikes of these 32 neurons:

x = torch.rand(size=[32]) * 4
T = 50
s_list = []
v_list = []
for t in range(T):

s_list =
v_list =

visualizing.plot_2d_heatmap(array=np.asarray(v_list), title='Membrane Potentials', xlabel='Simulating Step',
                            ylabel='Neuron Index', int_x_ticks=True, x_max=T, dpi=200)
visualizing.plot_1d_spikes(spikes=np.asarray(s_list), title='Membrane Potentials', xlabel='Simulating Step',
                           ylabel='Neuron Index', dpi=200)

The results are as follows:

../_images/1.svg ../_images/2.svg