Department of Psychology, University of Washington, Seattle, Washington, United States of America.
Institute for Neuroengineering, University of Washington, Seattle, Washington, United States of America.
PLoS Comput Biol. 2019 Jun 27;15(6):e1006908. doi: 10.1371/journal.pcbi.1006908. eCollection 2019 Jun.
Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.
有越来越多的证据表明，广泛的神经元反应可以被理解为非负稀疏编码（NSC）的一种涌现特性，这是一种基于降维和稀疏约束的高效群体编码方案。我们回顾了证据，表明 NSC 可能被感觉区域用来有效地编码外部刺激空间，被一些联想区域用来联合表示多个与行为相关的变量，并且可能被基底神经节用来协调运动。此外，NSC 可能提供了一个有用的理论框架，用于理解其他脑区中神经元的复杂和非直观的反应特性。尽管 NSC 可能不适用于所有脑区（例如，运动或执行功能区），但基于 NSC 的模型的成功，特别是在感觉区域，值得进一步研究其他区域的神经相关性。
