Neuroscience Beginner’s Guide
Embark on Your Neuroscience Journey: Essential Resources to Get Started
Welcome to our first blog post! We are BrAIn, a Neuroscience Reading Group at IIT Kharagpur.
The brain is truly fascinating. In today’s AI and Machine Learning era, while we strive to make computers smarter and more intelligent, we often encounter difficulties in replicating many tasks that the brain can effortlessly perform.
This blog is aimed at engineering students who share our fascination with the brain and are eager to learn more about it. We aim to introduce you to the intriguing world of Neuroscience by sharing a few valuable resources.
Basic Structure of the Brain
The brain is divided into various parts, each with its own specific functions. We encourage you to read this beautiful blog published by Johns Hopkins Institute to gain a better understanding.
Neuron Structure
Neurons are highly specialized for generating electrical signals in response to chemical and other inputs and transmitting them to other cells. Some important morphological specializations, seen in the drawing are the dendrites that receive inputs from other neurons and the axon that carries the neuronal output to other cells. The elaborate branching structure of the dendritic tree allows a neuron to receive inputs from many other neurons through synaptic connections. To dive into details of the anatomy of neurons check out this video by Khan Academy. Also, consider reading the blog to get an overview of neuron structure and function.
Ion Channels
It is important to have a basic understanding of the various ion channels present in the cell membrane of neurons. These ion channels are highly selective, allowing only specific ions to flow in and out of the neuron, and they play a major role in generating action potentials.
Action potential is an electrical signal that travels along the membrane of a neuron. Neurons process information, perform computations and communicate with each other through these action potentials.
Check out this excellent video from Harvard for a better understanding.
Single Neuron Modelling
- Leaky Integrate and Fire: The integrate-and-fire model is an extremely useful description of neuronal activity despite being the simplest model.
Watch this video to grasp an understanding of the model. Then you can follow this tutorial, and build up a leaky integrate-and-fire (LIF) neuron model and study its dynamics in response to various types of inputs (direct currents, Gaussian white noise, and Poisson spike trains, etc).
This model has a few missing things, one being the ion channels. It is the ion channels and their property that cause action potential so we will incorporate them in this model using the Hodgkin Huxley model.
2. Hodgkin-Huxley: Hodgkin-Huxley system of equations describes the spiking behaviour of neurons. We learned the Leaky integrate and fire model and we will change the model into a spiking model using Hodgkin-Huxley equations by incorporating voltage-dependent Na⁺ and K⁺ ion channels. To explain the behaviour of Action Potential the Na⁺ and K⁺ ion channels are sufficient. Hodgkin Huxley saw this in the Giant squid experiment. They did a patch clamp experiment on a giant squid axon and with a variety of measurements, came up with the exact property that governs the circuitry with the properties of Na⁺ and K⁺ ion channels.
You can have a look at this detailed video and blog on the Hodgkin-Huxley model of neurons.
Recording from the Brain
Various methods have been developed over the years to record brain activity. Single neuron recordings can be obtained using techniques such as Patch Clamp and Electrode arrays, while EEG technique is used to measure electrical activity from groups of neurons.
To learn more about this, we encourage you to read the following blogs written by Queensland Brain Institute.
Measuring Brain Activity in Animals & Measuring Brain Activity in Humans
Neural Coding
- Encoding vs Decoding:
Neural Encoding: Imagine your brain as a translator for the world around you. When you see something, hear a sound, or feel a touch, your brain has to turn those experiences into a language it can understand — electrical signals. This process is like how a translator changes words from one language to another so you can understand them.
So, neural encoding is like this translation process in your brain. It takes all the different things you sense — like sights, sounds, and feelings — and turns them into patterns of electrical signals that your brain can use to make sense of the world.
Neural Decoding: Now, think of neural decoding as the reverse process. After your brain has turned all those senses into electrical signals, it has to figure out what they mean. It’s like when you read a message in a foreign language and then translate it back into your own language to understand what it says.
In your brain, neural decoding is about interpreting those patterns of electrical signals to understand what you’re seeing, hearing, feeling, or thinking. It’s how your brain makes sense of the signals it created during encoding, so you can react, remember, or make decisions based on what you’ve sensed.
An overview of neural encoding and decoding is explained beautifully in this video.
2. Spike-Triggered Average- The spike-triggered average helps scientists understand what kinds of things make a neuron fire by looking at the average of the stimuli that were present just before each spike. It’s a way to uncover how neurons respond to specific features or events in their environment.
The spike-triggered average stimulus, C(τ), is the average value of the stimulus at a time interval τ before a spike is fired. In other words, for a spike occurring at time ti, we determine s(ti − τ), and then we sum over all n spikes in a trial, i = 1, 2,…, n and divide the total by n. For understanding in more detail, you can have a look at the book: Theoretical Neuroscience by Dayan and Abbott. (page 16–20)
3. Efficient Coding — Sparse Coding: Sparse coding is the study of algorithms that aim to learn a useful sparse representation of any given data.
Imagine you’re trying to store information efficiently. Instead of having a separate storage space for every single detail or piece of information, you organize things more smartly.
Sparse coding is like having a very organized filing system where:
- You only keep files (or neurons, in the brain’s case) for the most important or distinctive information.
- Each file (neuron) is very specialized — it’s responsible for storing a specific type of information or recognizing a particular pattern.
- By doing this, you use less space (fewer neurons) to store a lot of different types of information, because each neuron is doing a very specific job.
In neuroscience, this means that neurons in our brains are not all firing (active) all the time for every little thing we see, hear, or feel. Instead, certain neurons become active only when they detect something important or specific. This selective activation helps our brains process and understand complex information efficiently.
So, sparse coding is all about being efficient with how our brains process and store information by using specialized neurons that respond to specific patterns or features, rather than having every neuron respond to everything.
Synapse and Plasticity
Synapses are junctions between neurons where information is communicated chemically or electrically, crucial for enabling neurons to signal each other within the nervous system. Neurotransmitters are the primary messengers in this communication process. To know more about the different types of Neurotransmitters, we recommend this cute video. Hope you enjoy it as much as we did!
Synaptic plasticity refers to the ability of synapses to strengthen or weaken over time, which is crucial for learning and memory formation. There are two main types of synaptic plasticity: LTP (Long Term Potentiation) and LTD (Long Term Depression). Other types such as STDP (Spike Time Dependent Plasticity) also exist. We recommend going through this set of tutorials by Prof. Wulfram Gerstner at EPFL to gain a solid understanding.
An important learning rule used by the brain that we want to highlight is the Hebbian Learning rule which states that “Neurons that fire together wire together”. The set of tutorials linked above explains this in detail.
That brings us to the end of this blog. We will be back with more, soon! Happy Learning :)
We would love to hear your feedback! Please reach out to Yashaswini or Sohini Gupta to share your thoughts with us.