Garasto, StefaniaStefaniaGarastoNicola, WiltenWiltenNicolaBharath, AnilAnilBharathSchultz, SimonSimonSchultz2019-03-052019-05-202019http://hdl.handle.net/10044/1/67355Interpreting the neural code involves decoding the firing pattern of sensory neurons from the perspective of a downstream population. Performing such a read-out is an essential step for the understanding of sensory information processing in the brain and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to categorize stimuli using a predefined set of labels, less attention has been given to full-stimulus reconstruction, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of 103 neurons in layer 2/3 of mouse primary visual cortex. Using an optimal linear estimator, we investigated which factors drive the reconstruction performance at the pixel level. We find the density of receptive fields to be the most influential feature. Finally, we use the receptive field data and simulations from a linear-nonlinear Poisson model to extrapolate decoding accuracy as a function of network size. Based on our analysis on a public dataset, reconstruction performance using two-photon protocols might be considerably improved if the receptive fields are sampled more uniformly in the full visual field. These results provide practical experimental guidelines to boost the accuracy of full-stimulus reconstruction.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Science & TechnologyTechnologyLife Sciences & BiomedicineEngineering, BiomedicalNeurosciencesEngineeringNeurosciences & NeurologyNATURAL SCENESIMAGESConference Paperhttps://www.dx.doi.org/10.1109/NER.2019.8716934https://ieeexplore.ieee.org/document/8716934289146