Imaging the 4D choreography of subcellular events in living multicellular organisms at high spatiotemporal resolution could reveal life’s fundamental principles. Yet extracting these principles from petabyte-scale image data requires fusing advanced light microscopy and cutting-edge machine learning models with biological insight and expertise.

Cognitive flexibility, the ability to rapidly switch between different thoughts and mental concepts, is a highly advantageous human capability. This salient capability supports multi-tasking, the rapid acquisition of new skills and the adaptation to new situations.

While artificial intelligence (AI) systems have become increasingly advanced over the past few decades, they currently do not exhibit the same flexibility as humans in learning new skills and switching between tasks. A better understanding of how biological neural circuits support cognitive flexibility, particularly how they support multi-tasking, could inform future efforts aimed at developing more flexible AI.

Recently, some computer scientists and neuroscientists have been studying neural computations using artificial neural networks. Most of these networks, however, were generally trained to tackle specific tasks individually as opposed to multiple tasks.

Biological neural networks demonstrate complex memory and plasticity functions. This work proposes a single memristor based on SrTiO3 that emulates six synaptic functions for energy efficient operation. The bio-inspired deep neural network is trained to play Atari Pong, a complex reinforcement learning task in a dynamic environment.