I’ll add a strong plus one to this and a note for emphasis:
Representational geometry is already a long-standing theme in computational neuroscience (e.g. Kriegeskorte et al., 2013)
Overall I think mech interp practitioners would do well to pay more attention to ideas and methods in computational neuroscience. I think mech interp as a field has a habit of overlooking some hard-won lessons learned by that community.
I’ve no great resources in mind for this. Off the top of my head, a few examples of ideas that are common in comp neuro that might have bought mech interp some time if more people in mech interp were familiar with them when they were needed: - Polysemanticity/distributed representations/mixed selectivity - Sparse coding - Representational geometry I am not sure about what future mech interp needs will be, so it’s hard to predict which ideas or methods that are common in neuro will be useful (topological data analysis? Dynamical systems?). But it just seems pretty likely that a field that tackles such a similar problem will continue to be a useful source of intuitions and methods. I’d love if someone were to write a review or post on the interplay between the parallel fields of comp neuro and mech interp. It might help flag places where there ought to be more interplay.
I’ll add a strong plus one to this and a note for emphasis:
Representational geometry is already a long-standing theme in computational neuroscience (e.g. Kriegeskorte et al., 2013)
Overall I think mech interp practitioners would do well to pay more attention to ideas and methods in computational neuroscience. I think mech interp as a field has a habit of overlooking some hard-won lessons learned by that community.
I’m wondering if you have any other pointers to lessong/methods you think are valuable from neuroscience?
I’ve no great resources in mind for this. Off the top of my head, a few examples of ideas that are common in comp neuro that might have bought mech interp some time if more people in mech interp were familiar with them when they were needed:
- Polysemanticity/distributed representations/mixed selectivity
- Sparse coding
- Representational geometry
I am not sure about what future mech interp needs will be, so it’s hard to predict which ideas or methods that are common in neuro will be useful (topological data analysis? Dynamical systems?). But it just seems pretty likely that a field that tackles such a similar problem will continue to be a useful source of intuitions and methods. I’d love if someone were to write a review or post on the interplay between the parallel fields of comp neuro and mech interp. It might help flag places where there ought to be more interplay.