Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a context-dependent association task that requires linking sensory stimuli to behavioral responses and generalizing to multiple other symmetrical contexts. The model uses neural gating units that regulate the pattern of physiological connectivity within the circuit. These neural gating units can be used in a learning framework that performs low-rank matrix factorization analogous to recommender systems, allowing generalization with high accuracy to a wide range of additional symmetrical contexts. The neural gating units are trained with a biologically inspired framework involving traces of Hebbian modification that are updated based on the correct behavioral output of the network. This modeling demonstrates potential neural mechanisms for learning context-dependent association rules and for the change in selectivity of neurophysiological responses in the hippocampus. The proposed computational model is evaluated using simulations of the learning process and the application of the model to new stimuli. Further, human subject behavioral experiments were performed and the results validate the key observation of a low-rank synaptic matrix structure linking stimuli to responses.

中文翻译：

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受推荐系统启发的上下文关联任务的神经电路模型。

行为数据表明，人类和动物有能力学习应用于特定示例的关联规则，并将这些规则推广到广泛的环境中。本文重点介绍执行依赖于上下文的关联任务的神经回路机制，该任务需要将感官刺激与行为反应联系起来，并泛化到多个其他对称上下文。该模型使用神经门控单元来调节电路内的生理连接模式。这些神经门控单元可用于执行类似于推荐系统的低秩矩阵分解的学习框架，允许对广泛的额外对称上下文进行高精度泛化。神经门控单元使用生物学启发的框架进行训练，该框架涉及根据网络的正确行为输出更新的 Hebbian 修改痕迹。这种建模展示了学习上下文相关关联规则和海马神经生理反应选择性变化的潜在神经机制。使用学习过程的模拟和模型对新刺激的应用来评估所提出的计算模型。此外，还进行了人类受试者行为实验，结果验证了将刺激与反应联系起来的低阶突触矩阵结构的关键观察结果。这种建模展示了学习上下文相关关联规则和海马神经生理反应选择性变化的潜在神经机制。使用学习过程的模拟和模型对新刺激的应用来评估所提出的计算模型。此外，还进行了人类受试者行为实验，结果验证了将刺激与反应联系起来的低阶突触矩阵结构的关键观察结果。这种建模展示了学习上下文相关关联规则和海马神经生理反应选择性变化的潜在神经机制。使用学习过程的模拟和模型对新刺激的应用来评估所提出的计算模型。此外，还进行了人类受试者行为实验，结果验证了将刺激与反应联系起来的低阶突触矩阵结构的关键观察结果。