AI Study Shows Why Deep Learning Is Suited for Neuroscience

In the realm of artificial intelligence (AI), not all machine learning approaches are considered equal. This is an important consideration in fields such as neuroscience, medicine, biotechnology, life sciences, health care, genomics, pharmaceuticals, and other industries where accuracy may directly impact human health and safety. In a new study published earlier this month in Nature Communications, researchers at Georgia State University show the advantages of deep learning (DL) over standard machine learning (SML) in brain research. "Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain," wrote the paper's lead author Anees Abrol along with Sergey Plis, Vince Calhoun, Yuhui Du, Rogers Silva, Mustafa Salman, and Zening Fu. In standard machine learning, predictions are a result of processing prediction functions via inference rules, and the decision boundaries are determined in the "native, kernel-transformed, or feature-engineered input spaces."