Technology

A Tutorial on Spiking Neural Networks for Beginners

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Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures, deep learning (DL) tasks, and even brand new concepts of the next generation of NNs, such as the Spiking Neural Network (SNN).

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SNN was introduced by the researchers at Heidelberg University and the University of Bern developing as a fast and energy-efficient technique for computing using spiking neuromorphic substrates. In this article, we will mostly discuss Spiking Neural Network as a variant of neural network. We will also try to understand how is it different from the traditional neural networks. Below is a list of the important topics to be tackled.

Table of Contents

  • What is Spiking Neural Network (SNN)
  • How Does Spiking Neural Network Work?
  • Traditional Neural Network Vs SNN
  • Application of Spiking Neural Networks
  • Advantages and Disadvantages of SNN

Let’s start the discussion by understanding what is Spiking Neural Network is.

What is Spiking Neural Network (SNN)?

Artificial neural networks that closely mimic natural neural networks are known as spiking neural networks (SNNs). In addition to neuronal and synaptic status, SNNs incorporate time into their working model. The idea is that neurons in the SNN do not transmit information at the end of each propagation cycle (as they do in traditional multi-layer perceptron networks), but only when a membrane potential – a neuron’s intrinsic quality related to its membrane electrical charge – reaches a certain value, known as the threshold.

The neuron fires when the membrane potential hits the threshold, sending a signal to neighbouring neurons, which increase or decrease their potentials in response to the signal. A spiking neuron model is a neuron model that fires at the moment of threshold crossing.

SNN with connections and Biological Neuron

Artificial neurons, despite their striking resemblance to biological neurons, do not behave in the same way. Biological and artificial NNs differ fundamentally in the following ways

  • Structure in general
  • Computations in the brain
  • In comparison to the brain, learning is a rule. […]

Source: SwissCognitive

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