>Without sleep, humans can become forgetful, hallucinate, and even experience various physical and psychological problems. But new research published in the journal PLOS Computational Biology suggests that future AIs could benefit from getting some sleep too.

>Artificial neural networks often reach some superhuman heights, but when it comes to sequential learning, or learning one new thing after another, they become, well, kind of like Finding Nemo’s Dory. Unlike humans and animals who have the ability to learn and apply knowledge continuously, while these systems can certainly achieve excellence in a new task, it’s at the expense of the performance of a previous task. Once properly trained, it's very difficult to teach them a completely new task and if you succeed in training the new task, you end up damaging the old memory.

>In the neuro world, such an activity is called “catastrophic forgetting.” It’s an issue that can only be solved with something called “consolidation of memory,” a process that helps transform recent short-term memories into long-term ones, often occurring during REM sleep. This reorganization of memory might actually play a large part in why we need to sleep at all, especially as if the process does stop working, or is interrupted in some way, serious mental deficits can occur.

>To some, the concept is promising. As sleep is said to spike learning by enabling the “spontaneous reactivation of previously learned memory patterns,” the study notes that neuroscience-inspired artificial intelligence could actually be the next big thing. Building on previous work in memory plasticity and sleep modeling, the team used a neural network model to simulate sensory processing and reinforcement learning in an animal’s brain, and then gave it two separate tasks to complete. In both tasks, the network learned how to discriminate between being punished and being rewarded—enough so that eventually, it could make decisions on its own.