Neural network is counted as a noteworthy accomplishment in Artificial Intelligence (AI)

The human brain is one of the incredible wonders of the world. We often hear people comparing the human brain with the electronic computer and the neural network systems, as they have a lot of things in common. Henceforth, some scientists have tried to solve unanswered human brain-related questions using deep neural networks.

A neural network is a series of algorithms that endeavor to recognize underlying relationships in a set of data through a process that mimics how the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. The neural network is counted as a noteworthy accomplishment in Artificial Intelligence (AI). Henceforth, some neuroscientists used neural networks as a source in the development of computational models of brain functions.

Human brain and deep neural network

DiCarlo and Yamins run their own lab at Stanford University and are a part of a neuroscientist coterie using deep neural networks to make sense of the brain’s architecture. Understanding the functionalities of the human brain is a complex work. Human brain has specialized parts that detect various tasks. Neuroscientists have wondered how different parts of the brain do different things, but also how the difference could be so specific. Deep neural networks are showing that such specializations may be the most efficient way to solve problems.

Researchers have also demonstrated that the deep networks are proficient at classifying speech, music and simulated scents that seem to parallel the brain’s auditory and olfactory systems. Such parallels also show up in deep nets that can look at a 2D scene and infer the underlying properties of the 3D objects within it, which helps to explain how biological perception can be both fast and incredibly rich. All these results hint that living neural systems’ structures embody certain optimal solutions to the tasks they have taken on.

Here are a couple of neuroscientists’ findings that have long been skeptical of comparison between brains and deep neural networks.

Deep Nets and Vision: Artificial Neural Networks are built with interconnecting components called perceptrons, which are simplified digital models of biological neurons. The networks have at least two layers of perceptrons that are sandwiched by one or more ‘hidden’ layers between the input and the output. It forms a deep neural network. The greater the number of hidden layers, the deeper the network.

Deep nets can be trained to pick out patterns representing the images of cats or dogs. Deep nets have sometimes seemed like the best available option for modeling parts of the brain. The same kind of mechanism is what makes the human brain get visual content.

Specializing for sounds: Josh McDermott, a neuroscientist at Massachusetts Institute of Technology (MIT), along with his team, began designing deep nets to classify two types of sounds: speech and music. They hard-coded the cochlea model, the sound transducing organ in the inner ear, to process audio and sort the sounds into different frequency channels as inputs to a conventional neural network. This imitated the human hearing system and helped neuroscientists further their research in finding the mechanism behind sound detection in the human brain.