New York University Skip to Content Skip to Search Skip to Navigation Skip to Sub Navigation

NYU Scientists Develop Computer Model Explaining How Brain Learns to Categorize

March 11, 2015
270

New York University researchers have devised a computer model to explain how a neural circuit learns to classify sensory stimuli into discrete categories, such as “car vs. motorcycle.” Their findings, which appear in the journal Nature Communication, shed new light on the brain processes underpinning judgments we make on a daily basis.

“Categorization is vital for survival, such as distinguishing food from inedible things, as well as for formation of concepts, for instance ‘dog vs. cat,’ and relationship between concepts, such as hierarchical classification of animals,” says author Xiao-Jing Wang, Global Professor of Neural Science, Physics, and Mathematics at NYU and NYU Shanghai. “Our proposed model can only explain category learning of simple visual stimuli. Future research is needed to explore if the general principles extracted from this model are applicable to more complex categorizations.”

Wang conducted the study with Tatiana Engel, a postdoctoral associate at the time of the study, and Jah Chaisangmongkon, a doctoral candidate in his group, in collaboration with experimentalist David Freedman, a neurobiologist at the University of Chicago. Freedman had previously developed a behavioral paradigm for investigating electrical activity of single-neurons that are correlated with category memberships of visual stimuli.

In this neural-circuit model, which incorporates what we know about the organization and neurophysiology of the cortex, lower-level neural circuits send information about visual stimuli to a higher-level neural circuit where an analog stimulus feature (like the direction of a random pattern of moving dots) is classified into binary categories (A or B). The researchers’ results showed that the model captured a wide range of experimental observations and yielded specific predictions that were confirmed by an analysis of single-neuron electrical activity recorded in a category-learning experiment.

Interestingly, the researchers found that learning a correct category boundary (dividing the continuous feature into A and B) requires top-down feedback projection from category-selective neurons to feature-coding neurons.

Since the pioneering work by NYU’s J. Anthony Movshon, Stanford’s William Newsome, and others, it has been well known that feature-coding sensory neurons reflect an animal’s choice about categorical membership (A or B) of a stimulus in a probabilistic way (quantified as “choice probability”). The common belief was that this is because a category choice is influenced by stochastic, or random, activity of sensory neurons through bottom-up, sensory-to-category pathways.

The new model, reported in the Nature Communications article, suggests a novel interpretation, namely that such “choice probability” results from category-to-sensory, top-down signaling.

This finding offers new insights into feedback projections in the brain whose functional significance had previously been a long-standing puzzle, the researchers note.

This work was supported by the National Institute of Mental Health (R01MH092927) and the Swartz Foundation.
 

This Press Release is in the following Topics:
NYU Shanghai, Research, Arts and Science, Faculty

Type: Press Release

Press Contact: James Devitt | (212) 998-6808

NYU Scientists Develop Computer Model Explaining How Brain Learns to Categorize

NYU researchers have devised a computer model to explain how a neural circuit learns to classify sensory stimuli into discrete categories, such as “car vs. motorcycle." Their findings shed new light on the brain processes underpinning judgments we make on a daily basis. ©iStock/Jean Schweitzer


Search News


NYU In the News

Big Man on a Global Campus
The New York Times profiles NYU's new president, Andy Hamilton.
 
NYU Received a Record Number of Applications

Capital New York reported NYU received a record 60,322 applications for the class of 2019, an increase of about 15 percent since last year.

NYU Students Help City Crack Down on Hookah Bars

Capital New York reported that NYU students helped New York City crack down on hookah bars that illegally include tobacco in their hookahs:

Rudin Center Study Says Mass Transit Helps
Economic Mobility

The Wall Street Journal wrote about a report by Wagner’s Rudin Center that showed that mass transit could be more important than education in determining economic mobility.

Brennan Center Report Says Campaign Spending
Has Jumped

Frontline did a piece about a report by the Brennan Center for Justice that said that campaign spending by outside groups has more than doubled in the last five years.

NYU’s Dorms Ranked Among the Best in the Nation

Hometalk.com ranked NYU’s student residences third in the country in its list of best college dorms.

NYU Footer