Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

• Main Authors: Aurel A. Lazar, Nikul H. Ukani, Yiyin Zhou
• Format: Article
• Language: English
• Published: SpringerOpen 2018-01-01
• Series: Journal of Mathematical Neuroscience
• Subjects: Encoding of visual stimuli; Complex cells; Quadratic receptive fields; Dendritic stimulus processors; Sparse neural decoding; Sparse functional identification
• Online Access: http://link.springer.com/article/10.1186/s13408-017-0057-1

Abstract We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm 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 nonlinear input model, and the widely used spike-triggered covariance algorithm.