The Neural Newsletter 9/15-9/22

A powerful symbiotic relationship has blossomed between neuroscience and computer science as of late, with brain systems providing inspiration for prevalent computer algorithms like neural networks and computer-based mathematical models driving important research into the brain's computational methods. Daniel Kahneman's Thinking, Fast and Slow, has popularized the notion that human cognition is divided into distinct hierarchical systems, which Kahneman deems "system 1" and "system 2." Artificial intelligence can handle system 1 tasks, pertaining to fast, nonconscious operations, just as efficiently as humans can. However, it still lags behind when it comes to system 2 tasks, which engage different cognitive pathways that are slower and enlist conscious deliberation. The fact that computers can't compete with humans at deliberate tasks means that computer scientists still have a lot to learn from the brain, which inspired researchers out of the Sorbonne to develop a computational model based on the most recent theories in human learning and cognitive development. They found that processes like synaptic pruning (the elimination of underused synapses), neurogenesis, and energy regulation, and accurate dopamine reinforcement were underrepresented in computational learning models.