Summary: Persistency allows value signals to be most efficiently coded across the brain, specifically in the retrosplenial cortex.

Source: UCSD

In 2019 University of California San Diego researchers discovered the area of the brain where “value decisions” are made.

An area within the cerebrum known as the retrosplenial cortex (RSC), they found, is the site that we use to make value choices such as which restaurant we decide to visit for tonight’s dinner. We then update the RSC with fresh information based on the new impressions of how much we enjoyed the evening’s soup and pasta.

New research led by Division of Biological Sciences postdoctoral scholar Ryoma Hattori and Professor Takaki Komiyama is now revealing details about how such dynamic information is processed. The results, published Nov. 23 in the journal Neuron, show that persistency allows value signals to be most effectively represented, or “coded,” across different areas of the brain, especially the RSC.

To investigate the details of how brain activity represents value-based decision making, a core animal behavior that is impaired in neurological conditions such as schizophrenia, dementia and addiction, the researchers set up reinforcement learning experiments in which mice were presented with options and their choices were rewarded with certain probabilities. They recorded corresponding brain activities during the reinforcement learning.

The resulting data and network simulations pointed to the significance of persistent coding in how the mice and their value decisions were represented and the RSC as a nexus for this activity.

“These results suggest that although information coding is highly distributed, not all of the information represented in neural activity may be used in each area,” the authors explain in the paper. “These results reveal that context-dependent, untangled persistency facilitates reliable signal coding and its distribution across the brain.”

A new study is showing how value choices are recorded in our brains. Researchers found that persistency allows value signals to be most effectively represented, or “coded,” across different areas of the brain, especially in a critical area within the cerebrum known as the retrosplenial cortex. Credit: Komiyama Lab, UC San Diego

According to Hattori, neurons are known to cycle through different activity patterns, with some neurons spiking in activity and others remaining silent. 

These brain activity patterns have been shown to correlate to certain task-related information such as value information for decision making. Because the RSC plays a central role in connecting several brain networks and functions, the new findings reinforce ideas about the site’s fundamental importance.

“We think that in the mouse brain, the RSC functions as a stable reservoir for value information,” said Hattori. “The RSC appears to distribute value information to other brain areas that are vital for further processing of the value signals when mice perform reinforcement learning and decision making.”

To further test their findings, Hattori and Komiyama tapped into their “big data” trove of more than 100,000 mouse decisions recorded during the experiments. They programmed artificial intelligence (AI) networks to imitate behavioral strategies in computer-based reinforcement trials and found remarkably similar results to the real-world experiments.

“When we trained the artificial intelligence network to do the same behavior, it adopted the same strategy and the same way of representing the information in neural activity,” said Komiyama, who is a professor of neurobiology (Division of Biological Sciences) and neurosciences (Department of Neurosciences, School of Medicine), with affiliations in UC San Diego’s Center for Neural Circuits and Behavior and Halıcıoğlu Data Science Institute.

“This suggests that this is an evolutionarily selected strategy for neural circuits to perform this behavior. This parallel between the biological brain and the AI that Ryoma trained is really interesting.”

About this neuroscience research news

Author: Scott LaFee
Source: UCSD
Contact: Scott LaFee
Image: The image is credited to Komiyama Lab, UC San Diego

Original Research: Closed access.
“Context-dependent persistency as a coding mechanism for robust and widely distributed value coding” by Ryoma Hattori and Takaki Komiyama. Neuron


Abstract

See also

This shows a sad looking woman standing at a window

Context-dependent persistency as a coding mechanism for robust and widely distributed value coding

Highlights

  • Coding persistency in the cortex is learning and context dependent
  • Highly persistent value coding emerged in RSC and ANN during reinforcement learning
  • Persistency ensures untangled value representation within cylindrical geometry
  • Coding persistency facilitates brain-wide distributed information coding

Summary

Task-related information is widely distributed across the brain with different coding properties, such as persistency.

We found in mice that coding persistency of action history and value was variable across areas, learning phases, and task context, with the highest persistency in the retrosplenial cortex of expert mice performing value-based decisions where history needs to be maintained across trials.

Persistent coding also emerged in artificial networks trained to perform mouse-like reinforcement learning. Persistency allows temporally untangled value representations in neuronal manifolds where population activity exhibits cyclic trajectories that transition along the value axis after action outcomes, collectively forming cylindrical dynamics.

Simulations indicated that untangled persistency facilitates robust value retrieval by downstream networks. Even leakage of persistently maintained value through non-specific connectivity could contribute to the brain-wide distributed value coding with different levels of persistency.

These results reveal that context-dependent, untangled persistency facilitates reliable signal coding and its distribution across the brain.



Source link

LEAVE A REPLY

Please enter your comment!
Please enter your name here