Exploring the multiple loci of learning and computation in simple artificial neural networks

探索简单人工神经网络中学习和计算的多个位点

• 批准号: EP/X017915/1
• 负责人: Jeffrey Bowers
• 金额: $ 25.4万
• 依托单位: University of Bristol
• 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=EP%2FX017915%2F1
• 关键词: Exploring; multiple; loci; learning; computation

Two basic observations regarding neurons are: a) they emit a rapid sequence of action potentials when activated, known as spike-trains, and b) they vary dramatically in their morphology (e.g., their shape, size, etc.) in ways that impact on how they function. For example, neurons vary dramatically in how quickly they pass information along their axons (due to variation in axon diameter and myelination for example), and how long they integrate information from incoming signals (due to membrane time constants). By contrast, most deep neural networks (DNNs) developed in computer science do not include spiking units (artificial neurons) and all units are identical to one another other than their connection weights with one another. Nevertheless, DNNs are frequently described as the "best" models of human vision and are claimed to provide important insights into how the brain functions more generally. The assumption has been that DNNs can still be functionally equivalent to brains despite ignoring these features of neurons.However, there is growing evidence that DNNs often function in qualitatively different ways than brains, and this raises the important question as to how to make DNNs better models of the brain. And even if DNNs can be made functionally equivalent to brains, it is still important to understand how neural spiking and neural diversity are used for brain computation. In this project we harness evolutionary algorithms and state-of-the-art learning methods to train spiking neural networks that vary not only in the connection weights between units (as standard), but also the time it takes for units to pass on information (i.e., the conduction time of a neuron), the time that units can integrate signals (i.e., the time-constant of a neuron), as well as their intrinsic excitability (ease with which they fire). By allowing units to adapt/learn in all these different ways, we will be able to explore the advantages of learning outside the synapse under different training conditions, including tasks that involve identifying spatial patterns (e.g., written numbers) and temporal patterns (e.g., spoken words). It may be that adapting time-based attributes of neurons is particularly important when identifying temporal patterns. We hope the results will provide insight into the morphological variation of neurons, start to bridge the gap between artificial and biological neural networks, and the findings may identify computational advantages of learning and computing outside the synapse.

关于神经元的两个基本观察结果是：a）它们在被激活时发出快速的动作电位序列，称为尖峰序列，以及B）它们在形态上显著变化（例如，其形状、大小等）影响它们的功能。例如，神经元在沿着轴突传递信息的速度（例如由于轴突直径和髓鞘形成的变化）以及从传入信号中整合信息的时间（由于膜时间常数）方面存在显着差异。相比之下，计算机科学中开发的大多数深度神经网络（DNN）不包括尖峰单元（人工神经元），并且除了它们彼此之间的连接权重之外，所有单元彼此相同。尽管如此，DNN经常被描述为人类视觉的“最佳”模型，并声称对大脑如何更普遍地发挥作用提供了重要的见解。人们一直假设，尽管忽略了神经元的这些特征，但DNN在功能上仍然等同于大脑。然而，越来越多的证据表明，DNN通常以与大脑不同的方式发挥作用，这就提出了一个重要的问题，即如何使DNN成为更好的大脑模型。即使DNN可以在功能上等同于大脑，理解神经尖峰和神经多样性如何用于大脑计算仍然很重要。在这个项目中，我们利用进化算法和最先进的学习方法来训练尖峰神经网络，这些神经网络不仅在单元之间的连接权重（作为标准）方面不同，而且在单元传递信息所需的时间（即，神经元的传导时间），单元可以积分信号的时间（即，一个神经元的时间常数），以及它们内在的兴奋性（它们发射的容易程度）。通过允许单元以所有这些不同的方式适应/学习，我们将能够在不同的训练条件下探索突触外学习的优势，包括涉及识别空间模式的任务（例如，书写数字）和时间模式（例如，口语）。这可能是适应神经元的基于时间的属性在识别时间模式时特别重要。我们希望这些结果能够深入了解神经元的形态变化，开始弥合人工神经网络和生物神经网络之间的差距，并且这些发现可能会识别突触外学习和计算的计算优势。