Gepshtein Sergei, Pawar Ambarish S, Kwon Sunwoo, Savel'ev Sergey, Albright Thomas D
Systems Neurobiology Laboratories, Salk Institute for Biological Studies, La Jolla, CA, USA.
Center for Spatial Perception and Concrete Experience, University of Southern California, Los Angeles, CA, USA.
Sci Adv. 2022 Apr 22;8(16):eabl5865. doi: 10.1126/sciadv.abl5865.
The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here, we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Results of this process depend on interaction between stimulus dimensions. Comparison of modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.
传统观点认为，大脑皮层中的神经计算是指感觉神经元具有特异性，即对感觉刺激的某些维度具有选择性。然而，有证据表明刺激维度之间存在情境交互作用，即神经元对一个维度的反应强烈依赖于其他维度，这一观点受到了挑战。在此，我们运用数学建模、心理物理学和电生理学方法来解决传统观点的不足之处。我们使用一个通用皮层回路模型，首先进行了一个简单的演示，即皮层反应总是分布在神经元之间，形成特征波形，我们将其称为神经波。当受到模式化刺激时，回路反应通过神经波的干涉产生。这一过程的结果取决于刺激维度之间的相互作用。将模型反应与生物视觉反应进行比较后发现，神经波干涉框架为神经计算的标准概念提供了一个有用的替代方案。
