Recurrent neural networks (RNNs) are designed to learn sequential patterns in silico , but it is unclear whether and how an RNN forms in the native networks of the mammalian brain.
Here, we report an innate RNN, which is formed by the unidirectional connections from three basic units: input units arriving from emotion regions, a hidden unit in the medial prefrontal cortex (mPFC), and output units located at the somatic motor cortex (sMO). Specifically, the neurons from basal lateral amygdala (BLA) and the insular cortex (IC) project to the mPFC motor-cortex-projecting (MP) neurons. These MP neurons form a local self-feedback loop and target major projecting neurons of the sMO. Within the sMO, the neurons in the infragranular layers receive stronger input than the neurons in supragranular layers.
Finally, we show in vivo evidence that the communications from the emotion regions to the sMO are abolished when MP neurons are chemogenetically silenced.
- mPFC MP neurons consist of L2/3 CC neurons and L5 PT-CStr neurons
- MP neurons form a local, monosynaptic self-feedback loop in the mPFC
- mPFC MP neurons are necessary for communication from emotion regions to the sMO
- MP-PT-CStr neurons are the functional subtype in contacting emotion regions with the sMO
“The biggest surprise is that RNNs not only exist in our brain, but they are constructed with much more delicate function and, yet, highly efficient in processing sequential inputs,” Sun says.
“In general, cortical neurons are spatially reciprocal and intermingle with each other. However, Wang’s data not only showed that the RNN does exist in the most important part of the brain — the frontal cortex — but additionally, this network is less complex than we thought and mostly unidirectional. This is a big surprise to us, because this tells us that this network may be in charge of unique functions when compared with others.”
Sun and Wang analyzed the brains of mice for the lab research. Different genetically modified mouse strains provided the two with the ability to label specific types of neurons with fluorescent proteins that follow the brain’s connections — and to monitor the activities of specific neurons with intrinsically fluorescent markers.
The research has many real-world implications, according to Sun.
“One, now that we know of this important building block, the work will help further decipher how our brain makes decisions,” he says.
“Two, it will help uncover other similar RNNs in other parts of the brain. It will help researchers use computational simulations to predict how our brain codes short-term memory, and how can it be used. And three, specifically for this study, it will help us understand how emotions, such as fear and anxiety, regulate our movements.”