6. Working with Norse¶
For us, Norse is a tool to accelerate our own work within spiking neural networks. This page serves to describe the fundamental ideas behind the Python code in Norse and provide you with specific tools to become productive with SNNs.
We will start by explaining some basic terminology, describe a suggestion to how Norse can be approached, and finally provide examples on how we have solved specific problems with Norse.
6.1. Fundamental concepts¶
6.1.1. Functional vs. module packaging¶
As with PyTorch, the package is organised into a functional and module part. The functional package contains functions and classes that are considered atomic. If you use this package, you are expected to piece the code together yourself. The module package contains PyTorch modules on a slightly higher level.
We will work with the module abstraction in this document.
6.1.2. Neuron state¶
Neurons have parameters that determine their function. For example, they have a certain membrane voltage that will lead the neuron to spike if the voltage is above a threshold. Someone needs to keep track of that membrane voltage. If we wouldn’t, the neuron membrane would never update and we would never get any spikes. In Norse, we refer to that as the neuron state.
In code, it looks like this:
import torch import norse.torch as norse cell = norse.LIFCell() data = norse.ones(1) spikes, state = cell(data) # First run is done without any state ... spikes, state = cell(data, state) # Now we pass in the previous state
6.1.3. Neuron dynamics and time¶
Norse solves two of the hardest parts about running neuron simulations: neural equations and temporal dynamics. We have solved that by providing four modules for each neuron type. As an example, the Long short-term memory neuron model the four modules are:
The four modules represent the combinations between simulating over time and having recurrence.
In other words, the
LSNNCell is not recurrent, and expects the input data to not have time, while the
LSNNRecurrent is recurrent and expects the input to have time in the first dimension.
6.2. How to approach Norse¶
Norse is meant to be used as a library. Specifically, that means taking parts of it and remixing to fit the needs of a specific task. We have tried to provide useful, documented, and correct features from the spiking neural network domain, such that they become simple to work with.
The two main differences from artificial neural networks is 1) the state variables containing the neuron parameters and 2) the temporal dimension (see Introduction to spikes). Apart from that, Norse works like you would expect any PyTorch module to work.
When working with Norse we recommend that you consider two things
Learning algorithms and/or plasticity models
6.2.1. Deciding on neuron models¶
6.3. Examples on working with Norse¶
6.3.1. Porting deep learning models¶
A classical example of this can be seen in the MNIST where convolutions are brought into Norse.