Sparse coding models of neural response in the primary visual cortex

Computational neuroscience

Zhu, Mengchen ; Rozell, Christopher J. Biomedical Engineering (Joint GT/Emory Department) Nemenman, Ilya Butera, Robert J. Olshausen, Bruno A. Stanley, Garrett B. ; Rozell, Christopher J.

University:Georgia Institute of Technology

Department:Biomedical Engineering (Joint GT/Emory Department)

关键词: Computational neuroscience;   

Others  :  https://smartech.gatech.edu/bitstream/1853/53868/1/ZHU-DISSERTATION-2015.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Sparse coding is an influential unsupervised learning approach proposed as a theoretical model of the encoding process in the primary visual cortex (V1). While sparse coding has been successful in explainingclassical receptive field properties of simple cells, itwas unclear whether it can account for more complex response properties in a variety of cell types. In this dissertation, we demonstrate that sparse coding and its variants are consistent with key aspects of neural response in V1, including many contextual and nonlinear effects, a number of inhibitory interneuron properties, as well as thevariance and correlationdistributions in the population response. The results suggest that important response properties in V1 can be interpreted as emergent effects of a neural population efficiently representing the statistical structures of natural scenesunder resource constraints. Based on the models, we make predictions of the circuit structure and response properties in V1 that can beverified by future experiments.