Perceptual Decision Making “Through the Eyes” of a Large-Scale Neural Model of V1

Frontiers in Psychology

Jianing V. Shi¹ 

关键词: computational modeling;    decision making;    neuronal network;    sparse coding;   

DOI  :  10.3389/fpsyg.2013.00161

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given activity generated by a realistic mapping of the visual scene to neuronal spike trains, how do downstream neurons best utilize this representation to generate a “decision.” Specifically we consider both sparse (L1-regularized) and non-sparse (L2 regularized) linear decoding for mapping the neural dynamics of a large-scale spiking neuron model of primary visual cortex (V1) to a two alternative forced choice (2-AFC) perceptual decision. We show that while both sparse and non-sparse linear decoding yield discrimination results quantitatively consistent with human psychophysics, sparse linear decoding is more efficient in terms of the number of selected informative dimension.

【 授权许可】

CC BY