The researchers from the National University of Singapore are now successful in discovering a vital aspect of how a brain encodes short-term memories. Moreover, this is breakthrough discovery will help future research and studies in the field. The scientists are working in The N.1 Institute for Health at the National University of Singapore.
Assistant professor Camilo Libedinsky and Senior Lecturer Shin-Cheng Yen are leading the research. Camilo works at the Department of Psychology at NUS. Whereas Yen is from the Innovation and Design Program in the Faculty of Engineering at the same university.
According to their discovery, a group of neurons in the frontal lobe of the brain contains stable short term memory information. Furthermore, the researchers believe that this discovery may have long-term effects in providing inputs about how organisms are able to carry out different mental functions simultaneously. The results of the study are now featuring in the journal Nature Communications.
The researchers studied how the front lobe can represent short term memory information by keeping track of several neurons’ activities. Several previous studies showed that if a distraction occurs during the memory maintenance period, the brain changes the code by using frontal lobe neurons.
Machine Learning Tools to Solve Age-old Problem
With the help of machine learning, researchers are now successful in solving the problem of stable memory with changing codes. Thus, stable information in the brain resides inside the changing neural population code. Moreover, this discovery means that the NUS researchers are now able to demonstrate memory information out from groups of neurons. These neurons are the ones who morph their code if a distraction occurs.
This discovery now has far-reaching implications. It suggests that a single neuron may have several independent information types. Most importantly, these different information types do not interfere with each other. It may be a vital property of organisms that have the ability of cognitive flexibility.