The researchers believe that this discovery has wide-ranging implications for both neuroscience and artificial intelligence.

The brain uses data compression while making decisions.

If you grew up in the 1980s or like playing old video games, you might be familiar with Frogger. The game can be quite difficult. To succeed, you must first make it through a busy traffic flow and then zigzag through moving wooden planks to avoid certain death. How does the brain decide what to pay attention to amid this chaos?

A study published in the scientific journal Nature Neuroscience provides a possible solution: data compression.

“Compressing the representations of the external world is akin to eliminating all irrelevant information and adopting temporary ‘tunnel vision’ of the situation,” said one of the study’s senior authors Christian Machens, head of the Theoretical Neuroscience lab at the Champalimaud Foundation in Portugal.

“The idea that the brain maximizes performance while minimizing cost by using data compression is pervasive in studies of sensory processing. However, it hasn’t really been examined in cognitive functions,” said senior author Joe Paton, Director of the Champalimaud Neuroscience Research Programme. “Using a combination of experimental and computational techniques, we demonstrated that this same principle extends across a much broader range of functions than previously appreciated.”

The researchers employed a timing paradigm in their trials. Mice had to decide whether two tones were separated by a time greater or less than 1.5 seconds in each trial. While the animal was completing the challenge, the researchers simultaneously captured the activity of dopamine neurons in its brain.

“It is well known that dopamine neurons play a key role in learning the value of actions,” Machens explained. “So if the animal wrongly estimated the duration of the interval on a given trial, then the activity of these neurons would produce a ‘prediction error’ that should help improve performance on future trials.”

In order to determine which computational reinforcement learning model best captured both the activity of the neurons and the behavior of the animals, Asma Motiwala, the study’s first author, constructed a number of models. The models varied in how they represented the data that might be relevant for carrying out the task, but they shared certain common principles.

The group found that the data could only be explained by models with a compressed task representation.

“The brain seems to eliminate all irrelevant information. Curiously, it also apparently gets rid of some relevant information, but not enough to take a real hit on how much reward the animal collects overall. It clearly knows how to succeed in this game,” Machens said.

Interestingly, the type of information represented was not only about the variables of the task itself. Instead, it also captured the animal’s own actions.

“Previous research has focused on the features of the environment independently of the individual’s behavior. But we found that only compressed representations that depended on the animal’s actions fully explained the data. Indeed, our study is the first to show that the way representations of the external world are learned, especially taxing ones such as in this task, may interact in unusual ways with how animals choose to act,” Motiwala explained.

According to the authors, this finding has broad implications for Neuroscience as well as for Artificial Intelligence. “While the brain has clearly evolved to process information efficiently, AI algorithms often solve problems by brute force: using lots of data and lots of parameters. Our work provides a set of principles to guide future studies on how internal representations of the world may support intelligent behavior in the context of biology and AI,” Paton concluded.

Reference: “Efficient coding of cognitive variables underlies dopamine response and choice behavior” by Asma Motiwala, Sofia Soares, Bassam V. Atallah, Joseph J. Paton, and Christian K. Machens, 6 June 2022, Nature Neuroscience.
DOI: 10.1038/s41593-022-01085-7


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