Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
PLoS Comput Biol. 2024 Sep 3;20(9):e1012401. doi: 10.1371/journal.pcbi.1012401. eCollection 2024 Sep.
Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.
皮层中的神经活动表现出广泛的放电变异性和丰富的相关结构。神经编码的研究表明，相关的神经变异性可以有利或不利地影响神经编码的质量。然而，相关的神经变异性如何在神经群体之间转化和处理以实现有意义的计算，其机制在很大程度上仍不清楚。在这里，我们提出了一种带有尖峰神经元的协方差计算理论，为带有相关噪声的神经表示和计算提供了一个统一的视角。我们采用了一种最近提出的计算框架，称为矩神经网络，通过一种任务驱动的方法来构建用于执行基于协方差的感知任务的神经网络模型，从而解决相关神经变异性的非线性耦合问题。具体来说，我们展示了如何以近乎无损的方式将上游神经元尖峰活动的协方差中最初编码的感知信息传递到下游神经元的平均放电率，而下游神经元的平均放电率又可以用来进行推断。所提出的协方差计算理论解决了大脑如何从嘈杂的感觉刺激中提取感知信息以产生稳定的感知整体的重要问题，并表明相关变异性在皮质信息处理中起着更直接的作用。
