Bayesian inference in ring attractor networks.
Bayesian inference in ring attractor networks.
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DOI：
10.1073/pnas.2210622120
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发表时间：
2023-02-28
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
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作者：
来自人类和动物的数据表明，工作记忆与一种不确定感有关。事实上，不确定性使观察者能够正确地权衡新证据与他们当前的记忆。然而，我们并不了解大脑是如何追踪不确定性的。在这里，我们描述了一个简单的和生物学上合理的网络模型，可以跟踪与工作记忆相关的不确定性。与传统模型相比，该模型对不确定性的表示提高了其工作记忆的准确性，因为它对新的相互矛盾的证据赋予了适当的权重。我们的模型为观察到的大脑活动波动提供了解释，并做出了可测试的预测。工作记忆被认为保存在大脑的吸引网络中。这些吸引器应该跟踪与每个记忆相关的不确定性，以便在与之相冲突的新证据之间进行适当的权衡。然而，传统的吸引子并不代表不确定性。在这里，我们展示了如何将不确定性纳入吸引子，特别是编码头部方向的环形吸引子。首先，我们引入了一个严格的规范框架（圆形卡尔曼滤波器），用于在不确定条件下对环吸引子的性能进行基准测试。接下来，我们证明了常规环吸引子内的循环连接可以返回以匹配该基准。这使得网络活动的幅度在对确凿证据的反应中增加，而在对质量差或强烈冲突的证据的反应中缩小。这种“贝叶斯环吸引器”实现了近乎最优的角路径积分和证据积累。事实上，我们证明了贝叶斯环吸引子始终比传统的环吸引子更精确。此外，无需对网络连接进行精确调优，也可以实现近乎最优的性能。最后，我们使用大规模的连接组数据来表明，即使在我们纳入生物约束之后，网络也可以达到接近最佳的性能。我们的工作展示了吸引子如何以生物学上合理的方式实现动态贝叶斯推理算法，并且它做出了与头部方向系统以及任何跟踪方向、方向或周期节奏的神经系统直接相关的可测试预测。
Data from human subjects as well as animals show that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new evidence against their current memory. However, we do not understand how the brain tracks uncertainty. Here, we describe a simple and biologically plausible network model that can track the uncertainty associated with working memory. The representation of uncertainty in this model improves the accuracy of its working memory, as compared to conventional models, because it assigns proper weight to new conflicting evidence. Our model provides an interpretation of observed fluctuations in brain activity, and it makes testable predictions. Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.
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DOI：
10.1073/pnas.1117386109
发表时间：
2012-10-23
影响因子：
11.1
作者：
Burak, Yoram;Fiete, Ila R.
通讯作者：
Fiete, Ila R.
距离调整的神经元驱动内侧肠系膜内侧的专业路径积分计算。
影响因子：
8.8
作者：
Campbell MG;Attinger A;Ocko SA;Ganguli S;Giocomo LM
通讯作者：
Giocomo LM
影响因子：
4.3
作者：
Dehaene GP;Coen-Cagli R;Pouget A
通讯作者：
Pouget A
影响因子：
64.8
作者：
Fisher YE;Lu J;D'Alessandro I;Wilson RI
通讯作者：
Wilson RI
影响因子：
3.7
作者：
Cope AJ;Sabo C;Vasilaki E;Barron AB;Marshall JA
通讯作者：
Marshall JA