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兴奋-抑制平衡调节在多属性选择的分层神经网络模型中的决策策略。

Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice.

机构信息

Center for Neural Science, New York University, New York, United States of America.

Department of Psychiatry, Yale University School of Medicine, New Haven, United States of America.

出版信息

PLoS Comput Biol. 2021 Mar 11;17(3):e1008791. doi: 10.1371/journal.pcbi.1008791. eCollection 2021 Mar.

Abstract

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals.

摘要

我们经常面临着由多个属性定义的替代方案之间的决策,这需要对不同的信息源进行评估和整合。多个脑区的时变信号与决策有关;但我们缺乏严格的生物物理描述,无法说明基本的电路特性(如兴奋性-抑制性[E/I]平衡和级联非线性)如何塑造属性处理和选择行为。此外,这些特性如何在不同水平的环境不确定性下控制选择性能尚不清楚。我们在一个具有三个层次的动态级联非线性神经网络中研究了二属性、二选择决策:一个输入层,用于编码选择替代属性值;一个中间层,由处理独立属性的模块组成;以及一个最终层,产生决策。根据中间层 E/I 平衡,网络表现出不同的特征,由线性(I)、凸(II)或凹(III)的选择无差异曲线来描述。在 I 型和 II 型中,每个选项的属性信息是相加整合的。在 III 型中,时变非线性操作通过选择性地关注输入值差异较大的属性,放大了提供分布之间的分离。在低环境不确定性下,线性组合最能一致地选择具有更高价值的替代方案。然而,在高环境不确定性下,III 型比线性操作更有可能选择具有更高价值的替代方案。此外,在某些条件下,从中间层的读出可能与最终层无法区分。最后,这些原理被用于研究具有降低抑制性的多属性决策系统,导致对具有抑制性限制和神经典型的不同选择模式和整体性能的预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df0b/7987200/2711ee0fce3c/pcbi.1008791.g001.jpg

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