Research decodes animal decision-making with robotics and medical applications

Sat, 13 Sep 2025 16:32:21 -0400
ai, biology, machine-learning

A team of researchers, including Kathleen Hoffman, professor of mathematics and statistics at UMBC, is blending neuroscience, math, and engineering to decode animal decision-making, with potential to guide robots as they explore uncertain terrains or unlock secrets of the brain. The team’s research has just been funded by the Collaborative Research in Computational Neuroscience (CRCNS) program—a joint initiative of the NIH and the NSF.

The CRCNS program emphasizes collaborative efforts to advance understanding of nervous system functions through computational tools. With the lead investigator at JHU and additional collaborators at UMBC, the NJIT, and the University of Minnesota, the team for the newly funded project spans biology, engineering, mathematics, and computer science—a mix well-positioned to discover deeper insights into brain mechanisms.

In the prior work, “We looked at velocity distributions, and we found that there were two modes of movement. We called them ‘explore’ and ‘exploit,’ but you could also describe them as ‘fast’ and ‘slow,’” Hoffman explains. During experiments in narrow tubes, the fish alternated between two modes: rapid, exploratory movements to sense their surroundings (“explore”) and slower, deliberate actions using the information they’d collected (“exploit”).

A primary goal is to uncover what prompts the mode switch. “How does it decide when to switch? And the hypothesis that we’re considering is that it’s based on some internal measure of uncertainty in the fish, meaning that if the fish isn’t sure if it’s inside the tube, it’s going to move so it can gather sensory information,” Hoffman says.

To test this, the team integrates several methods. At the University of Minnesota, engineers led by Andrew Lamperski will apply machine learning to map relationships between sensory inputs and behavioral outputs in the form of mathematical functions. Hoffman handles data analysis, starting with manual pattern-spotting before coding.

Read more about this research here.