A new neuroscience study backed with funding from Wellcome and the European Research Council demonstrates how an AI deep learning algorithm is able to predict behavior by decoding brain activity. "The neural code provides a complex, non-linear representation of stimuli, behaviors, and cognitive states," wrote scientists affiliated with the Kavli Institute for Systems Neuroscience, the Max Planck Institute for Human Cognitive and Brain Sciences, UCL, and other institutions in eLife. "Reading this code is one of the primary goals of neuroscience – promising to provide insights into the computations performed by neural circuits." The decoding of brain data from imaging and neural recordings is a complex, time-consuming undertaking that the study's scientists characterize as "a non-trivial problem, requiring strong prior knowledge about the variables encoded and, crucially, the form in which they are represented." In efforts to decipher the neural code, the researchers created a convolutional neural network (CNN) to predict behaviors or other co-recorded stimuli from minimally processed, wide-band neural data.

Biological systems are inherently complex. Identifying patterns in biological systems is a daunting, time consuming endeavor. Biomedical engineers at Duke University have created a novel artificial intelligence (AI) machine learning methodology that can predict behaviors of biological circuits in orders of magnitude faster than standard computational methods, and published their findings in Nature Communications on September 25, 2019. In scientific research for pharmaceuticals, disease treatments, and biomedicine, mathematical modeling is used to understand the processes for the particular biological system. Different systems require a separate approach.