Simultaneous recordings from the cortex have revealed that neural activity is highly variable and that some variability is shared across neurons in a population. Further experimental work has demonstrated that the shared component of a neuronal population's variability is typically comparable to or larger than its private component. Meanwhile, an abundance of theoretical work has assessed the impact that shared variability has on a population code. For example, shared input noise is understood to have a detrimental impact on a neural population's coding fidelity. However, other contributions to variability, such as common noise, can also play a role in shaping correlated variability. We present a network of linear-nonlinear neurons in which we introduce a common noise input to model—for instance, variability resulting from upstream action potentials that are irrelevant to the task at hand. We show that by applying a heterogeneous set of synaptic weights to the neural inputs carrying the common noise, the network can improve its coding ability as measured by both Fisher information and Shannon mutual information, even in cases where this results in amplification of the common noise. With a broad and heterogeneous distribution of synaptic weights, a population of neurons can remove the harmful effects imposed by afferents that are uninformative about a stimulus. We demonstrate that some nonlinear networks benefit from weight diversification up to a certain population size, above which the drawbacks from amplified noise dominate over the benefits of diversification. We further characterize these benefits in terms of the relative strength of shared and private variability sources. Finally, we studied the asymptotic behavior of the mutual information and Fisher information analytically in our various networks as a function of population size. We find some surprising qualitative changes in the asymptotic behavior as we make seemingly minor changes in the synaptic weight distributions.

中文翻译：

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异构突触加权改善了存在常见噪声的神经编码

来自皮层的同时记录表明，神经活动是高度可变的，并且在群体中的神经元之间共享一些可变性。进一步的实验工作表明，神经元群体变异性的共享部分通常与其私有部分相当或更大。同时，大量的理论工作评估了共享可变性对种群代码的影响。例如，共享输入噪声被理解为对神经群体的编码保真度产生不利影响。然而，其他对变异性的贡献，例如共同噪声，也可以在塑造相关变异性方面发挥作用。我们提出了一个线性-非线性神经元网络，在其中我们向模型引入了一个共同的噪声输入——例如，由与手头任务无关的上游动作电位引起的可变性。我们表明，通过将一组异构突触权重应用于携带公共噪声的神经输入，网络可以提高其编码能力，如由 Fisher 信息和香农互信息测量的，即使在这导致公共噪声放大的情况下. 由于突触权重分布广泛且异质性，神经元群可以消除对刺激无信息的传入神经所施加的有害影响。我们证明了一些非线性网络从权重多样化中受益，达到一定的人口规模，超过这个规模，放大噪声的缺点超过了多样化的好处。我们根据共享和私有可变性源的相对强度进一步描述了这些好处。最后，我们在我们的各种网络中分析了互信息和 Fisher 信息的渐近行为，作为人口规模的函数。当我们对突触权重分布进行看似微小的变化时，我们发现渐近行为发生了一些令人惊讶的质的变化。