Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol. 2021 Oct 11;17(10):e1009479. doi: 10.1371/journal.pcbi.1009479. eCollection 2021 Oct.
A central question in neuroscience is how context changes perception. In the olfactory system, for example, experiments show that task demands can drive divergence and convergence of cortical odor responses, likely underpinning olfactory discrimination and generalization. Here, we propose a simple statistical mechanism for this effect based on unstructured feedback from the central brain to the olfactory bulb, which represents the context associated with an odor, and sufficiently selective cortical gating of sensory inputs. Strikingly, the model predicts that both convergence and divergence of cortical odor patterns should increase when odors are initially more similar, an effect reported in recent experiments. The theory in turn predicts reversals of these trends following experimental manipulations and in neurological conditions that increase cortical excitability.
神经科学的一个核心问题是上下文如何改变感知。例如，在嗅觉系统中，实验表明任务需求可以驱动皮质气味反应的发散和收敛，这可能是嗅觉辨别和泛化的基础。在这里，我们基于来自中枢大脑到代表与气味相关的上下文的嗅球的无结构反馈，以及对感觉输入的足够选择性的皮质门控，提出了这种效应的一种简单统计机制。引人注目的是，该模型预测当气味最初更相似时，皮质气味模式的收敛和发散都会增加，这是最近实验中报道的一种效应。该理论反过来又预测了在增加皮质兴奋性的实验操作和神经条件下，这些趋势的逆转。
