Emergent embodied cognition in shallow, biological and artificial, neural networks

浅层生物和人工神经网络中的突现认知

• 批准号: BB/X01343X/1
• 负责人: Paul Graham
• 金额: $ 25.49万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX01343X%2F1
• 关键词: Emergent; embodied; cognition; shallow; biological

Natural intelligence has been shaped by evolution for specific tasks in specific environments and to be adaptable to allow for lifelong rapid and robust learning. These properties of cognition are not reserved for animals with large brains. Insects such as ants and bees show impressive cognitive performance within their natural foraging behaviours. For instance, bees are expert navigators and can learn multiple complex routes based on visual memories. Similarly, bees are able to rapidly learn the floral patterns of rewarding flowers from which to collect nectar and pollen. We believe that the impressive performance of insects on such tasks arises from specialisation. That is, evolution has shaped sensors, neural circuits and behaviours for solving flower learning and navigation tasks. Consequently, bees show better-than-human performance on some tasks despite having brains a million times smaller. It is thus crucially important to understand how sensory systems and learning strategies can be adapted to tasks and environments, such that small neural circuits produce complex cognition. In this project, we will leverage recent advances in the speed of computational neuroscience simulations to systematically investigate the emergence of cognition in small neural networks and to tease apart the contribution of body, brain and environment. While these issues are relevant to all animals, we will focus on insects. It is easier to determine neuroanatomy and observe neurophysiology in insects than vertebrates, and we have detailed descriptions of the neural circuits involved in cognition (sensory lobes, learning centres and motor control regions). Furthermore, the specific behaviours where insects demonstrate impressive cognition are very well described in the lab and in the wild. In both these regards, our understanding of insects is more detailed and comprehensive than for vertebrate model systems. Insect brains are not only small but also shallow. There are only a few layers of processing between sensory systems and motor output. We hypothesise that these shallow learning networks work so well because they are interacting with carefully tuned sensory systems and behaviours. In order to decipher these complex interactions we will create a simulated world and spiking neural network model of key insect brain regions. With this model, we will investigate properties of visual cognition in tasks inspired by the foraging of bees. Our bee-like agents will move in a 3D simulated world, with a sensory system which replicates insect vision and learning implemented as models of the Mushroom Bodies (the insect learning circuits). Crucially, we will independently manipulate these components and use optimisation methods to ask what classes of sensory system and behaviour produce the best learning performance.Investigations of this kind have been envisioned before. However, to computationally determine the contribution of brain body and environment to cognition necessitates optimisation of multiple parameters at different levels of the agent. Optimisation methods for such multi-level problems exist (meta-learning or learning to learn algorithms) but common to these approaches is the need for very large numbers of evaluations. It is only now that we can explore these questions, in part due to increases in computational power, but also in conjunction with our recent breakthroughs in the speed of spiking neural network simulations on GPUs and insect-eye rendering technology. We therefore have the opportunity to investigate the relationships between sensory environment, brains and behaviour in detailed models of insect visual cognition. The understanding of how intelligence emerges from shallow neural networks will be fundamental to neuroscience and cognitive science, but also has the potential to produce more natural AI algorithms.

自然智能是通过特定环境中特定任务的进化而形成的，并且能够适应终生快速和稳健的学习。这些认知特性并不是为大脑较大的动物所保留的。蚂蚁和蜜蜂等昆虫在自然觅食行为中表现出令人印象深刻的认知表现。例如，蜜蜂是导航专家，可以根据视觉记忆学习多条复杂的路线。同样，蜜蜂能够快速学习奖励花朵的花型，并从中收集花蜜和花粉。我们相信昆虫在此类任务上的出色表现源于专业化。也就是说，进化塑造了用于解决花卉学习和导航任务的传感器、神经回路和行为。因此，尽管蜜蜂的大脑小一百万倍，但在某些任务上表现出比人类更好的表现。因此，了解感觉系统和学习策略如何适应任务和环境，从而使小型神经回路产生复杂的认知至关重要。在这个项目中，我们将利用计算神经科学模拟速度的最新进展，系统地研究小型神经网络中认知的出现，并梳理身体、大脑和环境的贡献。虽然这些问题与所有动物有关，但我们将重点关注昆虫。与脊椎动物相比，确定昆虫的神经解剖学和观察神经生理学更容易，并且我们对参与认知的神经回路（感觉叶、学习中心和运动控制区）有详细的描述。此外，昆虫表现出令人印象深刻的认知的具体行为在实验室和野外都有很好的描述。在这两方面，我们对昆虫的了解比脊椎动物模型系统更加详细和全面。昆虫的大脑不仅小而且浅。感觉系统和运动输出之间只有几层处理。我们假设这些浅层学习网络运作良好，因为它们与精心调整的感觉系统和行为相互作用。为了破译这些复杂的相互作用，我们将创建一个模拟世界和关键昆虫大脑区域的尖峰神经网络模型。通过这个模型，我们将研究受蜜蜂觅食启发的任务中的视觉认知特性。我们的类似蜜蜂的智能体将在 3D 模拟世界中移动，其感觉系统可以复制昆虫的视觉和学习能力，并以蘑菇体模型（昆虫学习电路）的形式实现。至关重要的是，我们将独立操纵​​这些组件，并使用优化方法来询问哪些类别的感觉系统和行为能够产生最佳的学习表现。以前已经设想过此类研究。然而，要通过计算确定脑体和环境对认知的贡献，需要优化代理不同级别的多个参数。存在针对此类多级问题的优化方法（元学习或学习算法），但这些方法的共同点是需要大量评估。直到现在我们才能够探索这些问题，部分原因是计算能力的提高，同时也结合了我们最近在 GPU 和昆虫眼渲染技术上的尖峰神经网络模拟速度方面的突破。因此，我们有机会在昆虫视觉认知的详细模型中研究感官环境、大脑和行为之间的关系。了解智能如何从浅层神经网络中产生将是神经科学和认知科学的基础，而且还有可能产生更自然的人工智能算法。