Foraging for information: Machine learning decodes genetic influence over behavior

ScienceDaily | 8/13/2019 | Staff
erinmmarion (Posted by) Level 3
"Patterns of complex behavior, like searching for food, are composed of sequences that feel random, spontaneous and free," said Christopher Gregg, Ph.D., assistant professor in Neurobiology and Anatomy at U of U Health and senior author of the study. "Using machine learning, we are finding discrete sequences that are reproduced more frequently than you would expect by chance and these sequences are rooted in biology."

The research team is venturing into the new territory of behavioral sequencing.

Architecture - Behavior - Genetics - Patterns - Gregg

"We are trying to understand the architecture of complex behavior and how genetics shape these patterns," said Gregg.

The research supports the idea that complex behavior is composed of a collection of finite 'building blocks' the authors call behavioral modules, and that genetics are controlling the progression of these building blocks to form different behavioral patterns.

Research - Team - Mice - Differences - Genetics

The research team evaluated 190 mice with differences in their genetics and age as they moved from their home into a uniquely created 'arena' to evaluate the set of behavioral sequences expressed while foraging for food. In the search for food, mice exhibit behaviors that require many neural systems to control seeking-behaviors, anxiety, reward, preservation, hunger, satiety, attention, navigation and memory. The new methods revealed that different genetic and age effects influence different sequences.

"Most species have a home range and their behaviors are structured around this home range," Gregg said. "We were able to identify reproduceable behavioral sequences and use this information to understand the complex patterns over time."

Team - Trips - Home - Food - Source

The team separated round trips from home to a food source and back into a series of more than 5,600 mouse actions. Layered within these actions are additional information, such as gait pattern, velocity, distance traveled and locations visited. Using machine...
(Excerpt) Read more at: ScienceDaily
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