Evaluating probabilistic inferential models of learnt sound representations in auditory cortex

人类、动物和一些人工智能（AI）系统都可以构建其感官环境的内部表示，以指导和通知他们的行动。从进化和工程的角度来看，良好的表现是那些促进灵活和适应性的行为结果。人工智能系统的训练通常涉及提供有关结果的反馈（强化或监督学习）。然而，关于行为结果的直接反馈在本质上是罕见的。因此，至少对于动物来说，良好的内部表征可能主要是通过无监督学习来形成的，这些学习来自感官输入中的统计学习。事实上，许多实验表明，动物的神经表征和行为可以通过被动暴露于改变的感觉环境而改变，特别是在早期或青少年发育期间。人工智能系统中的数据高效学习很可能最终也将依赖于有效的无监督学习算法。我们在这个项目中的目标是了解生物系统中感觉表征的无监督学习的计算原理，以及这些计算原理如何与人工智能系统中无监督学习算法的最新进展相关。我们将应用最先进的无监督推理方法来学习声学环境的概率模型，并评估这些模型在常规和改变的声学环境中饲养的动物的听觉皮层中再现神经记录的保真度。理解组织生物感知的统计原理可能会导致人工智能系统中更好的表征学习，而不需要强化或监督。相反，在人工智能系统中探索的高效、灵活的表征学习算法将有助于阐明生物系统中控制学习的计算原理。

Humans, animals, and some artificial intelligence (AI) systems can all build internal representations of their sensory environments that guide and inform their actions. From both evolutionary and engineering standpoints, good representations are those that facilitate flexible and adaptive behavioural outcomes. Training of AI systems often involves providing feedback about outcomes (reinforcement or supervised learning). However, direct feedback about behavioural outcomes is rare in nature. Thus, for animals at least, good internal representations may predominantly be shaped by unsupervised learning from statistical regularities in sensory input. Indeed, many experiments have shown that neural representations and behaviour in animals can be changed by passive exposure to altered sensory environments, especially during early or adolescent development. It is very likely that data-efficient learning in AI systems will also ultimately depend on effective unsupervised learning algorithms.Our goal in this project is to understand the computational principles underlying unsupervised learning of sensory representations in biological systems, and how those computational principles relate to recent advances in unsupervised learning algorithms for AI systems. We will apply state-of-the-art unsupervised inferential approaches to learn probabilistic models of acoustic environments, and evaluate the fidelity with which those models can reproduce neural recordings in the auditory cortex from animals raised in routine and altered acoustic environments. Understanding the statistical principles that organise biological perception is likely to lead to better representational learning in AI systems, without the need for reinforcement or supervision. Conversely, algorithms for efficient, flexible representational learning explored in AI systems will help to elucidate the computational principles governing learning in biological systems.