Network structure and input integration in competing firing rate models for decision-making
Network structure and input integration in competing firing rate models for decision-making
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DOI：
10.1007/s10827-018-0708-6
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发表时间：
2019-04-01
影响因子：
1.2
通讯作者：
Kawakita, Genji
中科院分区：
文献类型：
--
作者：
Barranca, Victor J.;Huang, Han;Kawakita, Genji
在众多的选择中做出决定是哺乳动物在自然环境中遇到的一项普遍而重要的任务。虽然双选项任务的决策已经在实验和理论上得到了广泛的研究，但在面对大量备选方案时，描述决策仍然具有挑战性。我们探索这个问题，制定一个可扩展的机制网络模型的决策和分析的动态引起各种潜在的网络结构。在一个完全连接的网络的情况下，我们提供了一个分析表征的模型不动点和他们的稳定性相对于赢家通吃的行为公平的任务。我们比较了几种输入积分的方法，证明了一个更渐进的S形传递函数可能相对于工程系统中常用的二进制增益在进化上是有利的。我们通过渐近分析和数值模拟表明，具有较小陡度的S形传递函数产生更快的响应时间，但精度下降。然而，在存在噪声或连接退化的情况下，S形传递函数获得了更强大和准确的决策动态。对于公平的任务和S形增益，我们的模型网络也表现出稳定的参数制度，产生高精度，并持续在不同数量的替代品和困难的任务，满足生理能量的限制。在更稀疏和结构化的网络拓扑结构的情况下，包括随机的，定期的，和小世界的连接，我们表明高精度的参数制度持续生物现实的连接密度。我们的工作表明，神经系统架构在跨任务做出经济、可靠和有利的决策方面是潜在的最佳选择。
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characterizing decision making in the face of a large set of alternatives remains challenging. We explore this issue by formulating a scalable mechanistic network model for decision making and analyzing the dynamics evoked given various potential network structures. In the case of a fully-connected network, we provide an analytical characterization of the model fixed points and their stability with respect to winner-take-all behavior for fair tasks. We compare several means of input integration, demonstrating a more gradual sigmoidal transfer function is likely evolutionarily advantageous relative to binary gain commonly utilized in engineered systems. We show via asymptotic analysis and numerical simulation that sigmoidal transfer functions with smaller steepness yield faster response times but depreciation in accuracy. However, in the presence of noise or degradation of connections, a sigmoidal transfer function garners significantly more robust and accurate decision-making dynamics. For fair tasks and sigmoidal gain, our model network also exhibits a stable parameter regime that produces high accuracy and persists across tasks with diverse numbers of alternatives and difficulties, satisfying physiological energetic constraints. In the case of more sparse and structured network topologies, including random, regular, and small-world connectivity, we show the high-accuracy parameter regime persists for biologically realistic connection densities. Our work shows how neural system architecture is potentially optimal in making economic, reliable, and advantageous decisions across tasks.