Two researchers at the University of Wyoming decided to pick their brains out, so to speak. Specifically, they examined the importance of the frontal cortex, the portion of the brain used in decision making, expressive language, and voluntary movement.
And the two scientists learned that a recurrent neural network structure, or RNN, is responsible for these functions.
“This RNN receives input from emotional regions of the brain and sends outputs to the motor cortex, the part of the brain responsible for voluntary movement,” says Qian-Quan Sun, a professor of zoology and physiology at UW. “In the field of artificial intelligence, computer scientists have designed various artificial neural networks, including RNNs, that effectively solve problems, such as language translation and object recognition, by simulating the neural network in the brain of mammals. .
“This paper provides a basic structure of neural networks in the mammalian brain. This basic structure will guide us in investigating behavioral strategy, “Sun continues.” After acquiring more details, we can translate it into an artificial neural network, using it to solve real-world problems. “
Sun, director of the Center for Excellence in Biomedical Research at Wyoming Sensory Biology UW, is the lead author of an article entitled “A long-range recurring neural network that connects regions of emotion with the somatic motor cortex” which was published today (Tuesday) in Cell Reports. The open access journal publishes peer-reviewed articles from across the life sciences spectrum reporting on a new biological vision.
The first author of the work is Yihan Wang, a doctorate. student of the UW Doctoral Neuroscience Program, Beijing, China. The research was funded by grants from the National Institutes of Health.
Artificial RNNs are important deep learning algorithms commonly used for temporal or ordinary lobe problems, such as language translation, natural language processing, speech recognition, and image title, says Sun. . An RNN recognizes the sequential characteristics of the data and uses patterns to predict the next likely scenario. RNNs are incorporated into popular applications such as Siri, Google Voice Search and Google Translate.
“The biggest surprise is that RNNs not only exist in our brain, but are built with a much more delicate function and yet very efficient in processing sequential inputs,” says Sun. “In general, cortical neurons are spatially reciprocal and mix with each other. However, Wang’s data not only showed that the RNN exists in the most important part of the brain, the frontal cortex, but also that this network is less complex than we thought and most is unidirectional. This is a big surprise for us, because it tells us that this network can handle unique functions compared to others. “
Sun and Wang analyzed the brains of mice for laboratory research. Different genetically modified mouse strains provided the two with the ability to label specific types of neurons with fluorescent proteins that track brain connections and to control the activities of specific neurons with intrinsically fluorescent markers.
According to Sun, research has many implications in the real world.
“One, now that we know this important basic element, work will help decipher even more how our brain makes decisions,” he says. “Two, it will help discover other similar RNNs in other parts of the brain. It will help researchers use computational simulations to predict how our brain encodes short-term memory and how it can be used. And three, specifically for this study, we they will help us understand how emotions, such as fear and anxiety, regulate our movements ”.
Both the content and the research approach used by Sun and Wang should have very broad interests with artificial intelligence researchers, biologists, computer modelers, and neuroscientists, Sun says.
“The precise connection map can also help us understand the cause of neurological and psychiatric disorders where there are problems with emotion regulation or voluntary movement,” Sun says. “However, before this finding can have wider applications, there are many details, such as how the local inhibitory network refined the RNN and how different components underlie specific states of emotion, that have yet to be discovered. “.
Wang’s goal is to resolve these details in his dissertation work, Sun says.
- Yihan Wang, Qian-Quan Sun. A long-range recurring neural network that connects regions of emotion with the somatic motor cortex. Cell Reports, 2021; 36 (12): 109733 DOI: 10.1016 / j.celrep.2021.109733