Neural correlates of sparse coding and dimensionality reduction


Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.

PLOS Computational Biology 15(6):e1006908
Michael Beyeler
Assistant Professor

Michael Beyeler directs the Bionic Vision Lab at UC Santa Barbara, which is developing novel methods and algorithms to interface sight recovery technologies with the human visual system, with the ultimate goal of restoring useful vision to the blind.