Research map / Probabilistic and scientific modeling / Dynamical systems
Scale-mixture Langevin sampling in the subspaces of recurrent cortical circuit dynamics
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Analyzes whether recurrent cortical circuit stochastic dynamics intrinsically implement a Bayesian posterior sampling algorithm, via scale-mixture Langevin sampling in circuit subspaces.
Problem
Identifying which sampling algorithm a recurrent cortical circuit intrinsically implements (rather than one mapped in by design) requires rigorous analysis of biological circuit dynamics, which is analytically intractable.
Approach
Analyzes the stochastic dynamics of recurrent cortical circuits in low-dimensional subspaces and characterizes them as scale-mixture Langevin sampling, rather than assuming a hand-designed sampler mapped onto the circuit.
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