Featured Research

from universities, journals, and other organizations

Neuroscience and big data: How to find simplicity in the brain

Date:
August 24, 2014
Source:
Carnegie Mellon University
Summary:
Scientists can now monitor and record the activity of hundreds of neurons concurrently in the brain, and ongoing technology developments promise to increase this number. However, simply recording the neural activity does not automatically lead to a clearer understanding of how the brain works. In a new article, researchers describe the scientific motivations for studying the activity of many neurons together, along with a class of machine learning algorithms for interpreting the activity.

Scientists can now monitor and record the activity of hundreds of neurons concurrently in the brain, and ongoing technology developments promise to increase this number manyfold. However, simply recording the neural activity does not automatically lead to a clearer understanding of how the brain works.

Related Articles


In a new review paper published in Nature Neuroscience, Carnegie Mellon University's Byron M. Yu and Columbia University's John P. Cunningham describe the scientific motivations for studying the activity of many neurons together, along with a class of machine learning algorithms -- dimensionality reduction -- for interpreting the activity.

In recent years, dimensionality reduction has provided insight into how the brain distinguishes between different odors, makes decisions in the face of uncertainty and is able to think about moving a limb without actually moving. Yu and Cunningham contend that using dimensionality reduction as a standard analytical method will make it easier to compare activity patterns in healthy and abnormal brains, ultimately leading to improved treatments and interventions for brain injuries and disorders.

"One of the central tenets of neuroscience is that large numbers of neurons work together to give rise to brain function. However, most standard analytical methods are appropriate for analyzing only one or two neurons at a time. To understand how large numbers of neurons interact, advanced statistical methods, such as dimensionality reduction, are needed to interpret these large-scale neural recordings," said Yu, an assistant professor of electrical and computer engineering and biomedical engineering at CMU and a faculty member in the Center for the Neural Basis of Cognition (CNBC).

The idea behind dimensionality reduction is to summarize the activity of a large number of neurons using a smaller number of latent (or hidden) variables. Dimensionality reduction methods are particularly useful to uncover inner workings of the brain, such as when we ruminate or solve a mental math problem, where all the action is going on inside the brain and not in the outside world. These latent variables can be used to trace out the path of ones thoughts.

"One of the major goals of science is to explain complex phenomena in simple terms. Traditionally, neuroscientists have sought to find simplicity with individual neurons. However, it is becoming increasingly recognized that neurons show varied features in their activity patterns that are difficult to explain by examining one neuron at a time. Dimensionality reduction provides us with a way to embrace single-neuron heterogeneity and seek simple explanations in terms of how neurons interact with each other," said Cunningham, assistant professor of statistics at Columbia.

Although dimensionality reduction is relatively new to neuroscience compared to existing analytical methods, it has already shown great promise. With Big Data getting ever bigger thanks to the continued development of neural recording technologies and the federal BRAIN Initiative, the use of dimensionality reduction and related methods will likely become increasingly essential.


Story Source:

The above story is based on materials provided by Carnegie Mellon University. Note: Materials may be edited for content and length.


Journal Reference:

  1. John P Cunningham, Byron M Yu. Dimensionality reduction for large-scale neural recordings. Nature Neuroscience, 2014; DOI: 10.1038/nn.3776

Cite This Page:

Carnegie Mellon University. "Neuroscience and big data: How to find simplicity in the brain." ScienceDaily. ScienceDaily, 24 August 2014. <www.sciencedaily.com/releases/2014/08/140824152349.htm>.
Carnegie Mellon University. (2014, August 24). Neuroscience and big data: How to find simplicity in the brain. ScienceDaily. Retrieved December 21, 2014 from www.sciencedaily.com/releases/2014/08/140824152349.htm
Carnegie Mellon University. "Neuroscience and big data: How to find simplicity in the brain." ScienceDaily. www.sciencedaily.com/releases/2014/08/140824152349.htm (accessed December 21, 2014).

Share This


More From ScienceDaily



More Computers & Math News

Sunday, December 21, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Building Google Into Cars

Building Google Into Cars

Reuters - Business Video Online (Dec. 19, 2014) Google's next Android version could become the standard that'll power your vehicle's entertainment and navigation features, Reuters has learned. Fred Katayama reports. Video provided by Reuters
Powered by NewsLook.com
After Sony Hack, What's Next?

After Sony Hack, What's Next?

Reuters - US Online Video (Dec. 19, 2014) The hacking attack on Sony Pictures has U.S. government officials weighing their response to the cyber-attack. Linda So reports. Video provided by Reuters
Powered by NewsLook.com
Navy Unveils Robot Fish

Navy Unveils Robot Fish

Reuters - Light News Video Online (Dec. 18, 2014) The U.S. Navy unveils an underwater device that mimics the movement of a fish. Tara Cleary reports. Video provided by Reuters
Powered by NewsLook.com
How 2014 Shaped The Future Of The Internet

How 2014 Shaped The Future Of The Internet

Newsy (Dec. 18, 2014) It has been a long, busy year for Net Neutrality. The stage is set for an expected landmark FCC decision sometime in 2015. Video provided by Newsy
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:

Strange & Offbeat Stories


Space & Time

Matter & Energy

Computers & Math

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins