In a new paper, we demonstrate biologically-plausible neural network models that can compute important features of predictive learning and memory systems. Our results suggest that these features are more accessible in neural circuits than previously thought, and can support a broad range of cognitive functions. The work achieves something that has proved difficult in AI research: bridging a well-defined computational function with its neural mechanism.

We’re introducing AutumnSynth, an algorithm that synthesizes the source code of simple 2D video games from a small amount of observed video data. This represents a step forward toward systems that can perform causal theory discovery in real-world environments.