With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.

中文翻译：

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利用行为多样性来理解认知的神经计算。

随着大规模神经记录的获取不断增加，推断它们执行的计算并理解它们如何产生行为的挑战也随之而来。在这里，我们回顾了开始应对这一挑战的新兴概念和技术进步，从生物和人工神经网络中获得见解。我们认为，应该在丰富的行为任务中记录神经数据，以建模认知过程并估计潜在的行为变量。对动物运动的仔细量化还可以更全面地了解运动如何塑造神经动力学并反映大脑状态的变化，例如唤醒或压力。人工神经网络（ANN）可以作为人工模型生物体来连接神经动力学和丰富的行为数据。人工神经网络已经开始揭示如何实施各种不同的行为，产生关于观察到的神经活动如何驱动行为的假设，并解释行为策略的多样性。