A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

神经科学的一个中心目标是理解大脑活动模式形成的表征及其与行为的联系。经典方法是研究单个神经元如何编码刺激，以及它们的调整如何决定神经表征的保真度。调谐分析通常使用 Fisher 信息来表征神经反应对刺激的微小变化的敏感性。近几十年来，对大量神经元的测量激发了一种补充方法，该方法侧重于线性解码器可用的信息。可解码信息由多元响应空间中代表性模式的几何形状捕获。在这里，我们回顾了神经调谐和表征几何，目的是阐明它们之间的关系。调谐会产生几何形状，但不同组的调谐神经元会产生相同的几何形状。几何决定了理想观察者在一系列心理物理学任务中的 Fisher 信息、互信息和行为表现。我们认为，未来的研究可以受益于同时考虑调整和几何学来理解神经代码并揭示刺激、大脑活动和行为之间的联系。