If behavior is a language, UO neuroscientist Luca Mazzucato decodes its grammar.

Distinct and coordinated activity in large sets of neurons can predict a rat’s future behavior, he and his team have shown in a new study. They created a dictionary that links patterns of brain activity to simple actions. The discovery helps them understand how the brain decides the timing of future actions and connects small actions into larger sequences.

He and his team reported their findings Oct. 29 in the journal Neuron.

“In this paper, we discovered what the brain’s laws are for creating simple behavioral sequences,” Mazzucato said.

Each complex behavior is made up of a series of simpler actions, just as a sentence can be deconstructed into stringed syllables of words. While there is flexibility in how these steps can be put together, there are also rules governing which combinations make sense and which do not.

Take walking across a room, for example. The pace of his steps may vary, but it would be unusual to see two left foot steps in a row. Predicting complicated behaviors requires understanding this grammar, both how the behaviors are constrained and how they are not.

Mazzucato wants to understand how an animal decides to take a certain action at a specific time, and how that choice is represented in the brain.

He and his team, led by postdocs Stefano Recanatesi and Ulises Pereira, partnered with neuroscientist Zachary Mainen at the Champalimaud Center for the Unknown in Lisbon, Portugal. They set up an experiment in which they trained rats to put their noses in one spot on the cage wall and then move to another spot in the cage to drink water. If the rats went for the reward immediately, they received a small sip; if they could wait 10 or 20 seconds they had more water. The rats’ series of actions was relatively fixed, but each animal could choose the timing.

As the rats dashed around, the researchers tracked the activity of large groups of neurons in a part of the brain called the motor cortex, which is involved in planning movements.

The rats’ future intentions were represented by the coordinated activity of large populations of neurons in the motor cortex, the researchers found. Each action created a distinct signature in the motor cortex, with a group of neurons behaving in a coordinated and coherent fashion. When a rat was about to switch to a different action, the neural signature suddenly changed.

“For every action the animal performed in the cage, we were able to understand what calculations the neurons were making in that brain,” Mazzucato said. “We could predict what the animal would do a few hundred milliseconds before it did.”

That is, they could distinguish between rats that went for the reward immediately and those that waited for a bigger glass of water, just by looking at brain activity.

They could also replicate the variability of rat choices through a set of mathematical equations called a recurrent neural network, an example of brain-inspired artificial intelligence. “Now that we have a model, we can generalize it,” Mazzucato said.

Here, the team focused on a single behavioral variable, the timing of actions. But in the future, they hope to investigate similar questions about choice and decision-making in scenarios that more closely mimic the complexity of real life.

Reference: Recanatesi S, Pereira-Obilinovic U, Murakami M, Mainen Z, Mazzucato L. Metastable attractors explain the variable timing of stable behavioral action sequences. neuron. 2021. doi: 10.1016/j.neuron.2021.10.011

This article was republished from the following materials. Note: Material may have been edited for length and content. For more information, please contact the quoted source.