Could a Neuroscientist Understand a Microprocessor?

Summary

• Addresses a popular belief in neuroscience that the field is primarily data limited, and producing large, multimodal datasets will lead to fundamental insights into the brain
• Use a classical microprocessor as a model organism and apply modern data analysis methods from neuroscience to understand its information processing
• These approaches reveal structure in the data but do not meaningfully explain information processing in the microprocessor
• Argue that neuroscientists should use complex non-linear dynamical systems with known ground truth (e.g. a microprocessor) to validate their data analysis methods
• Links: [ website ]

Background

• Difficult to evaluate effectiveness of various analysis methods in neuroscience due to lack of ground truth
• A known artificial system, the microprocessor, can act as a testbed for neuroscience methods
• Inspired by Yuri Lazbnick’s critique of modeling in molecular biology, “Could a biologist fix a radio?”

Methods

• Use MOS 6502 as “model organism”, with three “behaviors” (games): Donkey Kong, Space Invaders, and Pitfall
• Analogous to connectomics work, identify circuit structures and produce a transitor-accurate netlist, supporting a cycle-accurate simulator
  • 3510 transistors total
• What does an understanding look like? Hierarchical description of the functional modules
  • ALU, byte wide adder, binary adders, logic gates, transistors for the processor
  • Regions, circuits, microcircuits, neurons, synapses in the brain

Results

• Applying connectomics techniques reveal various transistor “types”, but in reality only one physical “type” of transistor
  • No way to go from anatomy to function, besides basic cell-type clustering
• Lesioning single transistors at a time demonstrates that a subset of the transistors are necessary for running the game (“behavior”)
  • Does this mean they’ve found “Donkey Kong transistors”?
• Plotting the “spike rate” of transistors as a function of the brightness of the most recently displayed pixel produces a few transistors with a strong tuning
  • Transistors relate in highly non-linear ways to the brightness of the screen
• Other methods like analyzing correlations, local field potentials, Granger causality, and dimensionality reduction likewise don’t produce satisfactory explanations

Conclusion

• Doesn’t actually present any potentially better methods
• Arguement for using systems with known ground truth to validate methods seems reasonable
• The “behavior” used is entirely passive