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Marc Howard runs his Theoretical Cognitive Neuroscience Lab at Boston University, where he develops mathematical models of cognition, constrained by psychological and neural data. In this episode, we discuss the idea that a Laplace transform and its inverse may serve as a unified framework for memory. In short, our memories are compressed on a continuous log-scale: as memories get older, their representations "spread out" in time. It turns out this kind of representation seems ubiquitous in the brain and across cognitive functions, suggesting it is likely a canonical computation our brains use to represent a wide variety of cognitive functions. We also discuss some of the ways Marc is incorporating this mathematical operation in deep learning nets to improve their ability to handle information at different time scales.
0:00 - Intro
4:57 - Main idea: Laplace transforms
12:00 - Time cells
20:08 - Laplace, compression, and time cells
25:34 - Everywhere in the brain
29:28 - Episodic memory
35:11 - Randy Gallistel's memory idea
40:37 - Adding Laplace to deep nets
48:04 - Reinforcement learning
1:00:52 - Brad Wyble Q: What gets filtered out?
1:05:38 - Replay and complementary learning systems
1:11:52 - Howard Goldowsky Q: Gyorgy Buzsaki
1:15:10 - Obstacles
