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Fundamentals of Artificial Neural Networks

Written by Mayuri Joshi

A new study allows the brain and artificial neurons to link up over the web's artificial intelligence and deep learning technology "Open Neuroscience".


Summary: Researchers have created a hybrid neural network where biological and artificial neurons in different parts of the world are able to communicate via the internet through a hub of Memristor synapses.


Neural networks are a subfield of machine learning where the algorithms are inspired by the structure of the human neural network to train themselves to recognize the patterns in the presented data and then predict the outputs for a new set of similar data.


Brain functions are made possible by circuits of spiking neurons, connected together by microscopic, but highly complex links called ‘synapses’. In this new study, published in the scientific journal Nature Scientific Reports, the scientists created a hybrid neural network where biological and artificial neurons in different parts of the world were able to communicate with each other over the internet through a hub of artificial synapses made using cutting-edge nanotechnology. This is the first time the three components have ever come together in a unified network. This map shows the communication locations of the virtual lab connecting Memristive synapses, the brain, and the silicon spiking neurons. Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics have made important advances to emulate neurons, synapses, and brain-computer interfacing concepts that interlink the brain and brain-inspired devices. We report on memristive links between brain and silicon spiking neurons that emulate the transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus.

Memristive plasticity accounts for modulation of connection strength, while the transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.


I have written several articles on Artificial Neural Networks earlier but they were just random articles on random concepts. This series of articles will give you a detailed idea about Artificial neural networks and concepts related to it. The resources and references to all the contents will be mentioned at the end of the series so you can study all concepts in depth.

So, let’s start with a very basic question. What is AI and what are artificial neural networks? In the very first article of the series, I will try to answer these basic questions, and then we will go ahead in depth in further articles.


What is Artificial Intelligence?


AI has been described as software that behaves in some limited ways like a human being. The word artificial comes from the Latin root word facere arte which means “make something” thus AI translates loosely to man-made intelligence. AI has been defined in many ways. Winston [1984] suggests one definition of AI as the study of ideas that enable computers to be intelligent. Rich and Knight [1991] define AI as the study of how to make computers do things which, at the moment, people do better.


The following are some more common definitions and/or descriptions of AI:

  1. AI is intelligent because it learns;

  2. AI transforms data into knowledge;

  3. AI is about intelligent problem solving;

  4. AI embodies the ability to adapt to the environment, to cope with incomplete or incorrect knowledge.


These revolutionary techniques fall under the AI field as they represent ideas that seem to emulate intelligence in their approach to solving commercial problems. All these AI tools have a common thread in that they attempt to solve problems such as the forecasting and explanation of financial markets data by applying physical laws and processes. Pal and Srimani [1996] state that these novel modes of computation are collectively known as soft computing as they have the unique characteristic of being able to exploit the tolerance imprecision and uncertainty in real-world problems to achieve tractability, robustness, and low cost. They further state that soft computing is often used to find an approximate solution.

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