Machine learning algorithms used to decode and enhance human memory

Machine learning algorithms used to decode and enhance human memory

  • March 5, 2018
Table of Contents

Machine learning algorithms used to decode and enhance human memory

When it comes to brain measurements, the best recordings come from inside the cranium. But people—and institutional review boards—aren’t usually amenable to cracking open skulls in the name of science. So Kahana and his colleagues collaborated with 25 epilepsy patients, each of whom had between 100 and 200 electrodes implanted in their brain (to monitor seizure-related electrical activity).

Kahana and his team piggybacked on those implants, using the electrodes to record high-resolution brain activity during memory tasks.

Source: wired.com

Share :
comments powered by Disqus

Related Posts

Learning by playing

Learning by playing

Our new paper proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome the issue of exploration in control tasks. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.

Read More
Machine Learning Workflow on Diabetes Data : Part 01

Machine Learning Workflow on Diabetes Data : Part 01

This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way.

Read More
So what’s new in AI?

So what’s new in AI?

I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.

Read More