Learning sparse codes from compressed representations with biologically plausible local wiring constraints Kion Fallah, Adam A. Willats, Ninghao Liu, Christopher J. Rozell

It is currently unknown if a randomized linear dimensionality reduction model for fiber projections in neural systems is feasible under biologically plausible local wiring constraints. The main contribution of this paper is to leverage recent results on structured random matrices to propose a theoretical neuroscience model of randomized projections for communication between cortical areas that is consistent with the local wiring constraints observed in neuroanatomy.