高秩连接支架支持递归神经网络中的精度和泛化

中文摘要 AI 辅助

神经科学和机器学习中的一个主要挑战是，在计算的因果解释中，将单个神经元的影响、群体动力学和电路连接联系起来。许多先前的研究表明，低秩连接可以在训练的人工神经网络中产生低维动力学，但这使得生物神经回路和许多人工神经元网络中更高秩结构的功能相关性不明确。在这里，我们分析了在一维和二维空间任务中，通过整合连续变化的速度输入来训练定位奖励的递归神经网络。我们发现，占主导地位的低维动力学编码任务位置，并且可以通过因果操作来指示行为结果。然而，在分解底层电路后，我们发现虽然低秩连接解释了低维动力学，但准确的性能和对新速度分布的泛化需要高秩连接。我们证明，这是通过分布式信号实现的，该信号纠正低维位置表示中的错误。因此，结合扰动、表征和电路层面的分析，展示了一种稳健空间计算的新机制，并展示了神经回路中的高秩连接如何提供支持精度和泛化的支架。

英文摘要

A major challenge in neuroscience and machine learning is to connect single-neuron influence, population dynamics, and circuit connectivity in a causal account of computation. Much previous work has shown that low-rank connectivity can generate low-dimensional dynamics in trained artificial neural networks, but this leaves unclear the functional relevance of the higher-rank structure of biological neural circuits and many artificial neuronal networks. Here we analyse recurrent neural networks trained to locate rewards by integrating continuously varying speed inputs in one- and two-dimensional spatial tasks. We find that dominant low-dimensional dynamics encode task locations, and can be causally manipulated to instruct behavioural outcomes. However, after decomposing the underlying circuitry we found that while low-rank connectivity accounts for the low-dimensional dynamics, accurate performance and generalisation to novel speed distributions requires high-rank connectivity. We demonstrate that this is achieved through distributed signalling that corrects errors in low-dimensional location representations. Thus, combined perturbation-, representation-, and circuit-level analyses demonstrate a novel mechanism for robust spatial computation and show how high-rank connectivity in neural circuits can provide a scaffold that supports precision and generalisation.

发表机构

• University of Edinburgh（爱丁堡大学）

机构由 AI 辅助整理，请以论文原文为准。