Livezey Jesse A, Sachdeva Pratik S, Dougherty Maximilian E, Summers Mathew T, Bouchard Kristofer E
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States.
Redwood Center for Theoretical Neuroscience, University of California, Berkeley, California, United States.
J Neurophysiol. 2025 Jan 1;133(1):124-141. doi: 10.1152/jn.00313.2024. Epub 2024 Nov 6.
The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is optimal for discriminative sensory coding. If the computational goal of sensory coding is discriminative, than correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found that correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves red discriminative coding performance suboptimal. Finally, we show that optimal subpopulations are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding. The brain represents the world through the activity of neural populations that exhibit correlated variability. We assessed optimality of correlated variability for discriminative sensory coding in diverse datasets by developing two novel null models. Across datasets, correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Biological constraints prevent the neural populations from achieving optimality. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.
大脑通过神经群体的活动来表征世界；然而，感觉编码的计算目标是支持对感觉刺激的辨别还是生成感觉世界的内部模型尚不清楚。在实验中通常会观察到神经群体间的相关变异性（噪声相关性），并且许多研究表明，与无相关性的零模型相比，相关变异性改善了辨别性感觉编码。然而，这些结果并未解决相关变异性对于辨别性感觉编码是否是最优的问题。如果感觉编码的计算目标是辨别性的，那么相关变异性应该被优化以支持该目标。我们通过开发两个具有生物学解释的新型零模型，评估了不同数据集中辨别性感觉编码的噪声相关性最优性。在各个数据集中，我们发现根据这两个零模型，神经群体中的相关变异性会导致高度次优的辨别性感觉编码。此外，生物学限制阻止神经群体的许多子集实现最优性，并且基于生物学标准进行子选择会使辨别性编码性能次优。最后，我们表明随着群体规模的增长，最优子群体呈指数级减小。总之，这些结果表明相关变异性的几何结构导致高度次优的辨别性感觉编码。大脑通过表现出相关变异性的神经群体的活动来表征世界。我们通过开发两个新型零模型评估了不同数据集中辨别性感觉编码的相关变异性最优性。在各个数据集中，根据这两个零模型，神经群体中的相关变异性会导致高度次优的辨别性感觉编码。生物学限制阻止神经群体实现最优性。总之，这些结果表明相关变异性的几何结构导致高度次优的辨别性感觉编码。
