Have you wondered how the brain works as a computer? And what it “practically” means... that the brain consists of neurons? What those biological neurons are, and what they do? How can we simulate biological neurons and build neural networks — called Spiking Neural Networks (SNNs) out of them? How learning happens in the brain, and how can we implement it? This topic covers the Computational Neuroscience basics - relevant to the SNNs, and answers all these questions in the following chapters:
1.1What You’ll Learn?¶
What is a Neuron?: What are biological neruons and how do they function? What role do the glial cells play? What biological characteristics of these neurons and cells should we simulate to build an SNN? This chapter follows through all these fundamental questions.
What is a Spiking Neuron?: Once you have learned about the biological neurons, the next step is to learn how to mathematically / programmatically simulate them and build Spiking Neuron models. This chapter covers spatial neuron models, point neuron models, and the relation of point neurons with conventional artificial neurons.
What is Encoding & Decoding?: Real-world data is almost always continuous-valued, and to work with SNNs, we need to represent / convert them to discrete integers. This chapter introduces the concept of Encoding, i.e., learn how to encode continuous values to spikes; and the concept of Decoding, i.e., learn how to decode meaningful information back from discrete spikes.
What is an SNN?: This chapter covers the foundations to build Fully Connected SNN and Convolutional SNN architectures from scratch - using Spiking Neurons and Deep Learning concepts. This chapter demonstrates only the forward pass; the backward pass / training is covered in the next topic “Training SNNs”.
What is plasticity?: Our brain is plastic, but what are the underlying neuroscience principles that facilitate learning in brain? This chapter covers the foundational concepts of neuroplasticity, e.g., Long-Term Potentiation (LTP) and Long-Term Depression (LTD) based on precise spike timings.