2017/06/19 by Aurel A. Lazar, Lazar, Aurel A., Nikul H. Ukani +3
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · #Advanced Fluorescence Microscopy Techniques #CCD and CMOS Imaging Sensors #FOS: Biological sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1706.05783
arxiv created 2017/06/19 · openalex publication_date 2017/06/19 · arxiv updated 2017/06/20 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
We investigate the sparse functional identification of complex cells and the decoding of visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm of both temporal and spatio-temporal stimuli is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification, and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the non-linear input model and the widely used spike-triggered covariance algorithm.