John Mccarthy, a computer scientist coined the term Artificial intelligence which replaced Pitts’ and McCulloch’s ‘nerve net’ and has since then been defined differently by experts throughout history. The combination of ‘human intelligence in machines’ is uniform in all these definitions.
Demis Hassabis who studied neuroscience for his PhD says, “I studied neuroscience for my PhD — to look into the brain’s memory and imagination; understand the mechanisms involved; and then [use that to] help us think about how we might achieve these same functions in our AI systems.”
Here is a list of the most famous brain inspired computing methods –
Artificial Neural Network – ANN
A neuron is like a tiny part of a brain-like computer system. It gets messages from other parts, does some math on them, and then sends out its own message. The connection between these parts is called a “synapse,” and how strong the connection is can be thought of as a “weight.”
The messages themselves are called “activations,” and the math the neuron does is a special kind of calculation. All these neurons working together make up something called an Artificial Neural Network (ANN). There are different kinds of these networks, like one that’s good at recognising pictures, one for patterns, and one for sequences of things.
Spiking Neural Networks – SNN
Imagine you have a regular computer system (called an artificial neural network or ANN) that does certain calculations. Now, with SNNs, there is an addition of something special: instead of just doing math, each part of the system can also “spike” like a little electrical signal, like in our brain.
This makes SNNs a bit like regular computer systems, but with an extra brain-like feature. Imagine if each part of the computer could send quick signals to each other. These signals can be simple or more complex, like how our brain’s cells communicate. Even the way these signals travel can be different, like how they move in our brain’s different parts.
These spiking neural networks can be used for lots of smart tasks like recognising images, understanding speech, and even making self-driving cars smarter. They’re a newer way of using computers that’s more energy-efficient and closer to how our brains work.
Neuromorphic computing
This is like making computer parts act like brain parts. It uses special chips with tiny circuits to copy how our brains work. These chips can do smart things like neurons in our brain. The term “neuromorphic” can mean different types of chips, whether they’re all electronic, a mix of electronic and other, or even just software. It’s like a special kind of brain-like computer that focuses on using tiny hardware pieces to do clever calculations.
Current AI systems that use artificial neural networks have some limitations. They keep data and calculations separate, and they use digital devices to represent continuous signals, which is not very natural. But there’s a new idea called neuromorphic computing that makes these systems work more like real brains. They use special parts called memristors, which are like little switches for electricity. These memristors can remember charges, acting like the connections in our brain. They’re good at handling complex data like images and videos, and they do it much faster and with less power than regular systems.
Scientists are making these memristors using different materials like graphene and even biological cells. They’re also looking at using light to process data, which can be super fast, but it’s still expensive to make those kinds of systems. Interestingly, even tiny living organisms like slime molds can do some similar things, and they might help in making these smart computing parts.
Reservoir computing
Just like neurons in our brain, the reservoir in reservoir computing processes data in a complex and parallel manner. When we receive input in our brains, it goes through a network of neurons that collectively transform the input into meaningful patterns. Similarly, in reservoir computing, the input data is processed by the interconnected units in the reservoir, which transforms the input into a more complex and informative representation.
The output of the reservoir, which is obtained after the data has passed through the reservoir’s internal dynamics, is analogous to the processed information in our brain. This output can be used for tasks like prediction, classification, and pattern recognition, similar to how our brains make sense of the world around us.
In essence, reservoir computing emulates the distributed and parallel processing observed in the brain, making it a brain-inspired approach to computing that leverages the principles of neural dynamics for various applications.
Quantum Computing
Quantum computing is a new way of making computers that are super good at solving really hard problems. Instead of using regular computer bits that are just 0 or 1, quantum computers use special bits called qubits. These qubits can be 0, 1, or both 0 and 1 at once, thanks to weird rules from the tiny world of quantum physics. This “both at once” thing makes quantum computers super fast at exploring many possibilities all at the same time.
They’re like super-smart puzzle solvers that can look at lots of answers together. But building and using quantum computers is tricky because they’re picky and need special conditions to work. Scientists are working on making them better, and when they’re ready, they could help solve problems in medicine, technology, and more.
Hyperdimensional Computing – HDC
Imagine a new way of doing computer stuff called hyperdimensional computing. Instead of using separate bits of info, like a car’s make, model, and coloUr, we put them all together as one thing called a “hyperdimensional vector.” This vector is like a special list of numbers.
Normal vectors have three numbers for x, y, and z (like 3D games). But hyperdimensional vectors can have lots more numbers, like 10,000! This helps computers do really smart things and goes beyond current limits. These special math things could change how we do computer smarts and artificial intelligence.
Like the brain it makes connections between things to understand and remember. It’s like teaching computers to think and remember in a creative and brain-like way, which helps them do smart tasks more efficiently. This makes connections like the brain.
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