Post #3195787

2025-01-23 17:09 UTC

@GabrielBena@neuromatch.social It could be the case that they're entirely separate: functional modules that don't overlap the structural modules at all. We often look for spatial maps in the brain, but the existence of salt-and-pepper maps shows that the brain doesn't have to organise spatially. It could be somewhere in between, with functional modules partially overlapping structural modules, which would explain why we can observe partial functional deficits after lesions to some but not all areas. Or maybe the brain isn't constrained to have anything that we would recognise as functional modularity at all? We won't get too deeply into that possibility, but we did ask what are the features we would expect based on our intuitions about modularity?

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@neuralreckoning@neuromatch.social 2025-01-23 17:10

@GabrielBena@neuromatch.social Some of these come from Fodor, and later Shallice and Cooper: modules should have a sub-function, respond to only one type of input, impairing them shouldn't impair other modules, and have limited access to information outside their state. Can we quantify these? We came up with three quantified measures of functional modularity based on: (1) probing (can we infer information a module shouldn't have from its activity), (2) ablation (which sub-functions are impaired), (3) dependency on data that should be irrelevant (with correlation). We also designed a task and network designed to have maximal, controllable modularity. There are two modules (dense recurrent neural networks) with sparse interconnections. Each receives a separate input. Solving the task requires they share precisely one bit of information. Roughly speaking, the task is that each module is given one digit to observe. If the parity of the two digits is the same (both even or both odd) then return the first digit, otherwise the second digit. You can solve this by having each module only communicate one parity bit to the other.

Open ##3195788