Redwood Center for Theoretical Neuroscience and Department of Physics, University of California, Berkeley, Berkeley, CA 94720 U.S.A., and Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.
Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA 94720, U.S.A., and Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.
Neural Comput. 2020 Jul;32(7):1239-1276. doi: 10.1162/neco_a_01287. Epub 2020 May 20.
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.
同时从大脑皮层记录显示，神经活动具有高度的可变性，并且在群体中的一些神经元之间存在可变性。进一步的实验工作表明，神经元群体可变性的共享成分通常与或大于其私有成分。与此同时，大量的理论工作评估了共享可变性对群体编码的影响。例如，共享输入噪声被认为对神经群体编码保真度具有不利影响。然而，其他可变因素，如常见噪声，也可以在塑造相关的可变性方面发挥作用。我们提出了一个线性非线性神经元网络，其中我们引入了一个共同的噪声输入来建模，例如，由于与手头任务无关的上游动作电位引起的可变性。我们表明，通过对携带共同噪声的神经输入应用一组异构的突触权重，网络可以提高其编码能力，如Fisher 信息和 Shannon 互信息所测，即使在这种情况会导致共同噪声放大的情况下。具有广泛而异构的突触权重分布，神经元群体可以消除对刺激没有信息量的传入的有害影响。我们证明，某些非线性网络受益于权重多样化，直到达到一定的群体大小，超过该大小，放大噪声的缺点超过多样化的好处。我们进一步根据共享和私有可变性源的相对强度来描述这些好处。最后，我们分析了我们的各种网络中互信息和 Fisher 信息的渐近行为，作为群体大小的函数。我们发现，在我们对突触权重分布进行看似微小的更改时，渐近行为会发生一些令人惊讶的定性变化。
