Neural Network Vs Human Brain: What is the Difference?

In this blog, we are going to study the difference between Artificial Neural Network Vs Human Brain, with different functionalities.

While it would be tempting to immediately dismiss the comparison as apples and oranges, it’s fairly common to see comparisons like this in articles on AI and machine learning.

This article will look at how artificial neural network are designed compared to their biological counterparts (vs Human Brain), what sets them apart, and how they function differently.

What is Artificial Neural Network?

Artificial neural networks are computer systems that contain a vast number of interconnected units.

Each of these units is called a neuron and they can exchange signals with one another through many different connections.

To better understand how artificial neural networks function, let’s talk about how human neural networks function.

As mentioned before, the human brain is composed of a vast network of interconnected neurons which is mostly similar vs a Neural Network.

There are two types of neurons within our brain interneurons and Externeurons.

Interneurons connect directly to other neurons while Externeurons connect to both other Externeurons and interneurons.

Externeuron cells also have an axon that connects them to other cells as well as dendrites that receive information from other cells.

Related Article: What is an Artificial Neural Network (ANN)?

What are Neurons in Human Brain?

Neurons are nerve cells that receive and send signals to other neurons, The human brain contains about 100 billion of them, so there’s a good chance you’ve got at least one neuron in your head right now.

There are many types of neurons, each responsible for a different action or feeling, like pain or hunger.

And although most neurons can connect with up to 10,000 other neurons, some interconnections have been found to have as many as 1 million connections.

Neurons are information processors, All of your thoughts, sensations, emotions, and memories can be traced back to electrical signals being sent from one neuron to another in your brain.

The process for generating these signals isn’t entirely understood yet, but it’s an active area of research.

Scientists have discovered that neurons contain a cell body (or soma), dendrites, and axon terminals.

When a neuron receives an impulse from another neuron, it sends out its impulse through its axon terminal this is how messages are relayed between neurons.

Artificial Neural Network Vs Human Brain

Researchers have created artificial neural networks (ANNs) that can outperform humans in a variety of tasks.

But what exactly are artificial neural networks and how do they work? In a nutshell, ANNs are inspired by biological neural networks in our brains.

While ANNs were initially designed to function like biological neural networks, it was later discovered that there are several key differences between these two types of systems.

This post aims to outline some of these major differences between artificial neural networks and human brains, To begin with, let’s take a look at how we typically define an artificial neural network.

An Artificial Neural Network (ANN) is a computational model based on an abstraction of neurons and synapses.

The artificial neurons are arranged in layers, where each layer receives input from those below it and sends output to those above it.

The connections between artificial neurons are referred to as synapses, which can be either excitatory or inhibitory.

Artificial neural networks were initially designed to function like biological neural networks, but there are several key differences between these two types of systems.

Related Article: Neural Network Exam Questions and Answers

Key Differences Between Neural Network vs Human Brain

1. Receptive Field

The receptive field of a neuron can be thought of as a filter that determines what that particular neuron will receive. As such, there are two different receptive fields involved in perception.

First, each eye has its visual field which consists of our viewable surroundings. Second, each ear has an auditory field that consists of our audible surroundings.

Each area is responsible for sending information to specific parts of our brain; while they overlap somewhat, they also send their signals to different areas within our brain.

2. Signal Propagation

Artificial neural networks are fed with a massive amount of data to learn, While artificial neural nets were initially designed to function like biological neural networks.

One of these is signal propagation – how fast information can travel from one point to another.

The neural activity in our brains is far more complex than might be suggested by simply studying artificial neurons.

While they were initially created to mimic biological processes in nature when placed side by side with an artificial neuron and a biological neuron there are obvious differences.

3. Synaptic Transmission

One important difference between an artificial neural network and a human brain lies in synaptic transmission.

The brain contains billions of synapses, and each synapse forms its connection with thousands of other neurons.

This web-like pattern allows for immense complexity and information processing within each brain.

Furthermore, neuroscientists still don’t understand exactly how information travels through our brain or why we can do things like think or feel emotions.

Artificial neural networks are often used to replicate patterns from these systems in machines.

4. Scaling Up – How Do We Get So Many Neurons?

The human brain has a whopping 86 billion neurons, and with each neuron connecting to up to 10,000 other neurons, it’s safe to say that we have an incredibly complex network of neural activity.

While scientists are still learning about how these networks function and how they differ between species and humans we can safely say that our brain is far more complex than even artificial neural networks.

It may be difficult for us to imagine just how many connections there are in our brains, but we can look at some statistics from Google.

Google processes 40,000 searches per second; if you consider that every search on Google represents a single connection between two people or computers (which is not entirely accurate), then Google processes over 1 trillion connections per day! That’s roughly 250 trillion connections per year.

5. Central Pattern Generator (CPG)

A central pattern generator (CPG) or central pattern generator network is a neural network that generates rhythmical output, such as walking and swimming.

CPGs consist of three main subunits: a periodic oscillator, an activator, and a modulator. The PGO wave (also called stimulus artifact) plays an important role in initiating REM sleep.

It has been suggested that it may be produced by a specific neural network located in the brainstem reticular formation, which is known as ponto-genicular-occipital waves (PGO). This hypothesis was based on experiments using cats and rabbits.

Artificial Neural Networks Are Not as Complex as Real Brains

The concept of artificial neural networks was initially inspired by biological neural networks, but it turns out that artificial neural nets are not nearly as complex as real brains. Understanding why requires a brief look at how each works.

The word neural comes from a neuron, which is another name for a brain cell or neuron.

There are many different types of neurons, but they all have two parts: dendrites and axons. Dendrites receive signals from other neurons and axons transmit signals to other neurons.

The space between two neurons where information is transmitted is called a synapse.

When you hear someone having an idea pop into their head, what happened was probably that synapse firing off in their brains!

All About Spiking Neurons

Just as there are many kinds of neurons and synapses in our brains, artificial neural networks come in a variety of forms.

While it’s not necessary to understand how every artificial neural network works, it is helpful to know what kind of artificial neuron an AI uses.

The three most common types of artificial neurons are binary, sigmoid, and spiking.

1. Binary neurons are those that simply output 0 or 1 they can be either off or on.

2. Sigmoid neurons use a function called a sigmoid curve to output values between 0 and 1.

3. Spiking neural networks use individual spikes (instead of numbers) to represent information this allows them to handle data more efficiently than other types of neural nets.

Conclusion

The human brain contains approximately 86 billion neurons, while artificial neural networks typically contain anywhere from thousands to billions of simulated neurons.

While even a simple neural network functions in a manner that’s similar to how we perceive and learn new information.

It is important to note that artificial neural networks are nowhere near as complex as what we find in our brains.

Each neuron within an artificial neural network can only perform one specific task (such as multiplying numbers or adding two numbers together), which means there’s no way for these networks to function like a true biological neural network.

That said, researchers continue to work on improving artificial neural networks so they might one day become more like real-life human brains.