Abstract
Discovering the neural mechanisms underpinning cognition is one of the grand challenges of neuroscience. However, previous approaches for building models of RNN dynamics that explain behaviour required iterative refinement of architectures and/or optimisation objectives, resulting in a piecemeal, and mostly heuristic, human-in-the-loop process. Here, we offer an alternative approach that automates the discovery of viable RNN mechanisms by explicitly training RNNs to reproduce behaviour, including the same characteristic errors and suboptimalities, that humans and animals produce in a cognitive task. Achieving this required two main innovations. First, as the amount of behavioural data that can be collected in experiments is often too limited to train RNNs, we use a non-parametric generative model of behavioural responses to produce surrogate data for training RNNs. Second, to capture all relevant statistical aspects of the data, we developed a novel diffusion model-based approach for training RNNs. To showcase the potential of our approach, we chose a visual working memory task as our test-bed, as behaviour in this task is well known to produce response distributions that are patently multimodal (due to swap errors). The resulting network dynamics correctly qualitative features of macaque neural data. Importantly, these results were not possible to obtain with more traditional approaches, i.e., when only a limited set of behavioural signatures (rather than the full richness of behavioural response distributions) were fitted, or when RNNs were trained for task optimality (instead of reproducing behaviour). Our approach also yields novel predictions about the mechanism of swap errors, which can be readily tested in experiments. These results suggest that fitting RNNs to rich patterns of behaviour provides a powerful way to automatically discover mechanisms of important cognitive functions.
Abstract (translated)
探索神经机制背后的认知原理是神经科学面临的重大挑战之一。然而，以往构建能够解释行为的RNN（递归神经网络）动态模型的方法需要对架构和/或优化目标进行迭代改进，导致了一个零散且主要依赖直觉的人工干预过程。在这里，我们提供了一种替代方法，通过明确训练RNN来复制人类和动物在认知任务中产生的行为及其特征性错误和次优表现，从而自动化发现可行的RNN机制。 实现这一目标需要两个主要创新： 1. **生成模型**：由于实验中收集的行为数据量通常不足以训练RNN，我们使用非参数生成模型来产生用于训练RNN的替代数据。 2. **扩散模型**：为了捕捉数据的所有相关统计特性，我们开发了一种基于扩散模型的新方法，用于训练RNN。 为展示我们方法的潜力，我们选择了一个视觉工作记忆任务作为测试平台，因为在该任务中行为产生的反应分布显然是多模态（由于交换错误）。由此产生的网络动态正确地再现了猕猴神经数据中的定性特征。重要的是，这些结果通过传统的做法是无法获得的，例如当仅拟合有限的行为特征集而不是完整的行为响应分布时，或者当RNN被训练以实现任务最优而非复制行为时。 我们的方法还提供了关于交换错误机制的新预测，这些预测可以在实验中直接测试。这些结果表明，将RNN拟合到丰富的行为模式上可以自动发现重要认知功能的机制，为理解大脑如何支持复杂认知过程提供了一种强有力的方法。
URL
https://arxiv.org/abs/2512.04808
