Neuroscience-Inspired Artificial Intelligence

This 2017 Neuron review is Hassabis's intellectual manifesto — his clearest articulation of why neuroscience should be the primary inspiration for building artificial intelligence, rather than statistics or formal logic. The paper surveys intersections between neuroscience and AI across five domains: attention, episodic memory, working memory, continual learning, and imagination/planning. Hassabis argues that the brain solved intelligence once, so studying it closely is the best path to solving it again artificially. Key examples: the hippocampus and episodic memory inspired DeepMind's Neural Episodic Control; the visual cortex's hierarchical structure inspired deep convolutional networks; the reward circuits and dopamine signaling inspired modern RL algorithms (TD learning is literally a model of dopamine-based prediction error). The paper contrasts this approach with the 'blank slate' RL approach — Hassabis argues that useful inductive biases from neuroscience can bootstrap learning significantly, even when the ultimate goal is to surpass human performance. This framing explains why Google DeepMind maintains a neuroscience division and why Hassabis considers the brain-AI co-inspiration to be a long-term bidirectional relationship, not just a historical origin story.