Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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How machine learning is helping us to understand the brain

By Nand Kishor |Email | Nov 26, 2017 | 13002 Views

The workings of the brain are the greatest mystery in science. Unlike our models of physics, strong enough to predict gravitational waves and unseen particles, our brain models explain only the most basic forms of perception, cognition, and behaviour. We know plenty about the biology of neurons and glia, the cells that make up the brain. And we know enough about how they interact with each other to account for some reflexes and sensory phenomena, such as optical illusions. But even slightly more complex levels of mental experience have evaded our theories.

We are quickly approaching the point when our traditional reasons for pleading ignorance - that we don't have the right tools, that we need more data, that brains are complex and chaotic - will not account for our lack of explanations. Our techniques for seeing what the brain and its neurons are doing, at any given moment, get stronger every year.

But we are using the wrong set of metaphors to describe the entire field, basing our understanding of the brain on comparisons to communications fields, like signal processing and information theory. Going forward, we should leave that flawed language choice behind. Instead, the words and ideas needed to unlock our brains come from a computational field much nearer to real biology: the expanding world of machine learning.

Homines ex machine?
For most of its history, "systems" neuroscience - the study of brains as large groups of interacting neurons - has tried to frame perception, action, and even cognition in terms taken from fields like signal processing, information theory, and statistical inference. Because these frameworks were essential for developing communications technology and data-processing algorithms, they suggested testable analogies for how neurons might communicate with each other or encode what we perceive with our senses. Many discussions in neuroscience would sound familiar to an audio engineer designing an amplifier: a certain region of the brain "filters" the sensory stimulus, "passing information" to the next "processing stage."

Source: Salon