Featured Research

from universities, journals, and other organizations

Understanding collective animal behavior may be in the eye of the computer

Date:
January 16, 2014
Source:
New York University Polytechnic School of Engineering
Summary:
An international team of researchers is the first to successfully apply machine learning toward understanding collective animal behavior from raw data such as video without tracking each individual. The findings stand to significantly impact the field of ethology -- the objective study of animal behavior -- and may prove as profound as the breakthroughs that allowed robots to learn to recognize obstacles and navigate their environment.

No machine is better at recognizing patterns in nature than the human brain. It takes mere seconds to recognize the order in a flock of birds flying in formation, schooling fish, or an army of a million marching ants. But computer analyses of collective animal behavior are limited by the need for constant tracking and measurement data for each individual; hence, the mechanics of social animal interaction are not fully understood.

An international team of researchers led by Maurizio Porfiri, associate professor of mechanical and aerospace engineering at NYU Polytechnic School of Engineering, has introduced a new paradigm in the study of social behavior in animal species, including humans. Their work is the first to successfully apply machine learning toward understanding collective animal behavior from raw data such as video, image or sound, without tracking each individual. The findings stand to significantly impact the field of ethology -- the objective study of animal behavior -- and may prove as profound as the breakthroughs that allowed robots to learn to recognize obstacles and navigate their environment. The paper was published online today in Scientific Reports, an open-access journal of the Nature Publishing Group.

Starting with the premise that humans have the innate ability to recognize behavior patterns almost subconsciously, the researchers created a framework to apply that instinctive understanding to machine learning techniques. Machine learning algorithms are widely used in applications like biometric identification systems and weather trend data, and allow researchers to understand and compare complex sets of data through simple visual representations.

A human viewing a flock of flying birds discerns both the coordinated behavior and the formation's shape -- a line, for example -- without measuring and plotting a dizzying number of coordinates for each bird. For these experiments, the researchers deployed an existing machine learning method called isometric mapping (ISOMAP) to determine if the algorithm could analyze video of that same flock of birds, register the aligned motion, and embed the information on a low-dimensional manifold to visually display the properties of the group's behavior. Thus, a high-dimensional quantitative data set would be represented in a single dimension -- a line -- mirroring human observation and indicating a high degree of organization within the group.

"We wanted to put ISOMAP to the test alongside human observation," Porfiri explained. "If humans and computers could observe social animal species and arrive at similar characterizations of their behavior, we would have a dramatically better quantitative tool for exploring collective animal behavior than anything we've seen," he said.

The team captured video of five social species -- ants, fish, frogs, chickens, and humans -- under three sets of conditions -- natural motion, and the presence of one and two stimuli -- over 10 days. They subjected the raw video to analysis through ISOMAP, producing manifolds representing the groups' behavior and motion. The researchers then tasked a group of observers with watching the videos and assigning a measure of collective behavior to each group under each circumstance. Human rankings were scaled to be visually comparable with the ISOMAP manifolds.

The similarities between the human and machine classifications were remarkable. ISOMAP proved capable not only of accurately ascribing a degree of collective interaction that meshed with human observation, but of distinguishing between species. Both humans and ISOMAP ascribed the highest degree of interaction to ants and the least to frogs -- analyses that hold true to known qualities of the species. Both were also able to distinguish changes in the animals' collective behavior in the presence of various stimuli.

The researchers believe that this breakthrough is the beginning of an entirely new way of understanding and comparing the behaviors of social animals. Future experiments will focus on expanding the technique to more subtle aspects of collective behavior; for example, the chirping of crickets or synchronized flashing of fireflies.


Story Source:

The above story is based on materials provided by New York University Polytechnic School of Engineering. Note: Materials may be edited for content and length.


Journal Reference:

  1. Pietro DeLellis, Giovanni Polverino, Gozde Ustuner, Nicole Abaid, Simone Macrì, Erik M. Bollt, Maurizio Porfiri. Collective behaviour across animal species. Scientific Reports, 2014; 4 DOI: 10.1038/srep03723

Cite This Page:

New York University Polytechnic School of Engineering. "Understanding collective animal behavior may be in the eye of the computer." ScienceDaily. ScienceDaily, 16 January 2014. <www.sciencedaily.com/releases/2014/01/140116131019.htm>.
New York University Polytechnic School of Engineering. (2014, January 16). Understanding collective animal behavior may be in the eye of the computer. ScienceDaily. Retrieved September 16, 2014 from www.sciencedaily.com/releases/2014/01/140116131019.htm
New York University Polytechnic School of Engineering. "Understanding collective animal behavior may be in the eye of the computer." ScienceDaily. www.sciencedaily.com/releases/2014/01/140116131019.htm (accessed September 16, 2014).

Share This



More Plants & Animals News

Tuesday, September 16, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Some Tobacco Farmers Thrive Amid Challenges

Some Tobacco Farmers Thrive Amid Challenges

AP (Sep. 16, 2014) — The South's tobacco country is surviving, and even thriving in some cases, as demand overseas keeps growers in the fields of one of America's oldest cash crops. (Sept. 16) Video provided by AP
Powered by NewsLook.com
Scientists Given Rare Glimpse of 350-Kilo Colossal Squid

Scientists Given Rare Glimpse of 350-Kilo Colossal Squid

AFP (Sep. 16, 2014) — Scientists say a female colossal squid weighing an estimated 350 kilograms (770 lbs) and thought to be only the second intact specimen ever found was carrying eggs when discovered in the Antarctic. Duration: 00:47 Video provided by AFP
Powered by NewsLook.com
Raw: Scientists Examine Colossal Squid

Raw: Scientists Examine Colossal Squid

AP (Sep. 16, 2014) — Squid experts in New Zealand thawed and examined an unusual catch on Tuesday: a colossal squid. It was captured in Antarctica's remote Ross Sea in December last year and has been frozen for eight months. (Sept. 16) Video provided by AP
Powered by NewsLook.com
Conservationists Face Uphill PR Battle With New Shark Rules

Conservationists Face Uphill PR Battle With New Shark Rules

Newsy (Sep. 14, 2014) — New conservation measures for shark fishing face an uphill PR battle in the fight to slow shark extinction. 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:
from the past week

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