Quantifying Information Conveyed by Large Neuronal Populations.
Quantifying Information Conveyed by Large Neuronal Populations.
复制标题
DOI：
10.1162/neco_a_01193
复制
发表时间：
2019-06
影响因子：
2.9
通讯作者：
Sharpee TO
中科院分区：
文献类型：
--
作者：
Berkowitz JA;Sharpee TO
量化大型神经回路输入和输出之间的互信息是机器学习和神经科学中的一个重要的开放问题。然而，众所周知，由于需要评估的项数量呈指数增长，对于大型系统来说，互信息的评估通常很困难。在这里，我们展示了如何有效地计算大型神经群体的响应中包含的信息，前提是单个神经元的输入输出函数可以通过应用于刺激的潜在非线性函数的逻辑函数来测量和近似。该模型中的神经反应可以对多种刺激成分保持敏感。我们表明，该模型中的互信息可以有效地近似为低维条件互信息项的总和。在大型神经群体的限制以及神经群体中感受野分布的某些条件下，近似变得精确。我们凭经验发现，即使不满足感受野分布的条件，这些近似仍然有效。所提出方法的计算成本在输入维度上线性增长，并且与其他近似方法相比具有优势。
Quantifying mutual information between inputs and outputs of a large neural circuit is an important open problem in both machine learning and neuroscience. However, evaluation of the mutual information is known to be generally intractable for large systems due to the exponential growth in the number of terms that need to be evaluated. Here we show how information contained in the responses of large neural populations can be effectively computed provided the input-output functions of individual neurons can be measured and approximated by a logistic function applied to a potentially nonlinear function of the stimulus. Neural responses in this model can remain sensitive to multiple stimulus components. We show that the mutual information in this model can be effectively approximated as a sum of lower-dimensional conditional mutual information terms. The approximations become exact in the limit of large neural populations and for certain conditions on the distribution of receptive fields across the neural population. We empirically find that these approximations continue to work well even when the conditions on the receptive field distributions are not fulfilled. The computing cost for the proposed methods grows linearly in the dimension of the input, and compares favorably with other approximations.
登录
查看更多内容
影响因子：
16.6
作者：
Dettner, Amadeus;Muenzberg, Sabrina;Tchumatchenko, Tatjana
通讯作者：
Tchumatchenko, Tatjana
影响因子：
64.8
作者：
Schneidman, E;Berry, MJ;Bialek, W
通讯作者：
Bialek, W
影响因子：
64.8
作者：
Sharpee, TO;Sugihara, H;Miller, KD
通讯作者：
Miller, KD
影响因子：
1.8
作者：
Bell, AJ;Sejnowski, TJ
通讯作者：
Sejnowski, TJ
影响因子：
64.8
作者：
Smith, EC;Lewicki, MS
通讯作者：
Lewicki, MS