Abstract: The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning models using biological systems (e.g. brain data) to engineer machine-learning-algorithms that can generalize better. Particularly, aspects are directed to a computer implemented method that includes measuring a plurality of biological responses (e.g. neural responses to stimuli or other variables such body movements); generating data (e.g. responses to stimuli) using the predictive model which can denoise biological data and extract task relevant information; scaling and transforming these predictions (e.g. measure representational similarities between stimuli); and using the biologically derived data to regularize machine-learning-algorithms.

Abstract: An apparatus and method for detecting functional cellular activity within a volume of a tissue. The method includes inserting a three-dimensional array of optical emitters and optical detectors into a volume of a tissue, where the tissue volume includes one or more cells labeled with an optical reporter of cellular activity; illuminating the one or more cells with photons from the optical emitters of the three-dimensional array to generate optical signals from the optical reporter that labels the one or more cells; and detecting the optical signals using the optical detectors of the three-dimensional array, where the illumination includes one-photon excitation of the optical reporter.