Large-scale neural models of cognitive function

认知功能的大规模神经模型

• 批准号: RGPIN-2015-04495
• 负责人: Eliasmith, Chris
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650067
• 关键词: Large; scale; neural; models; cognitive

***We have developed methods for simulating cognitive behaviour using biologically plausible spiking neurons that have resulted in what is currently the world's largest functional brain model, Spaun, which was recently published in the journal Science (Eliasmith et al., 2012). This model is the culmination of work that is described fully in the recent book "How to build a brain" (Eliasmith, 2013), which outlines a general method and architecture for constructing such models called the Semantic Pointer Architecture (SPA). The over-riding challenge that we now encounter, and the focus of this proposal, is scaling up both the theory and the simulations to tackle cognitive tasks in more demanding circumstances. ******Our central objective is to build biologically detailed models of cognition. We have identified three main challenges that we need to address to retain our position as leaders in the field. First, no group has yet employed highly realistic single neuron models (e.g. compartmental, conductance based models) in large-scale cognitive models while capturing psychological functions. Doing so provides the promise of better characterizing the relationship between biological and psychological properties allowing us to test new medical interventions (e.g. drugs, deep brain stimulation, etc.). Second, biologically based cognitive models are, by their very nature, large in scale and computationally demanding. Consequently, we intend to develop simulation tools and specialized hardware infrastructure that allow for significant speed and scale improvements, rivaling the best in the world. Third, understanding and exploiting the critical role that adaptation plays in a large, complex, functioning system is difficult. Challenges stem from ensuring that the system remains stable during adaptation, generalizing learned behaviours across tasks at the cognitive level, and understanding the relations between different kinds of adaptation evident across different domains (e.g. perception, motor control, and cognition). Addressing these three broad challenges will allow us to construct the next generation of `whole-brain' models.******This research will further test a new theoretical framework for integrating perceptual, motor, and cognitive behaviour in biologically plausible models. The proposed research will significantly improve the adaptability and functionality of SPA models, and have the important practical consequence of more directly connecting models to medical interventions that can help with the diagnosis, prevention, and repair of damage to neural systems. Finally, understanding the unique solutions that nature has found for difficult real-world problems should pave the way for innovative engineering solutions in intelligent systems, pattern recognition, and decision-making applications.

* 我们已经开发出使用生物学上合理的尖峰神经元来模拟认知行为的方法，这些方法已经导致了目前世界上最大的功能性大脑模型Spaun，该模型最近发表在《科学》杂志上（Eliasmith等人，2012年）。该模型是在最近的书“如何构建大脑”（Eliasmith，2013）中充分描述的工作的顶峰，该书概述了用于构建此类模型的通用方法和架构，称为语义指针架构（SPA）。我们现在遇到的首要挑战，也是该提案的重点，是扩大理论和模拟的规模，以在更严格的环境下解决认知任务。****** 我们的主要目标是建立详细的生物认知模型。我们已经确定了我们需要应对的三个主要挑战，以保持我们在该领域的领导地位。首先，还没有一个研究小组在大规模认知模型中采用高度逼真的单神经元模型（例如，基于隔室的电导模型），同时捕获心理功能。这样做可以更好地描述生物和心理特性之间的关系，使我们能够测试新的医疗干预措施（例如药物，深部脑刺激等）。其次，基于生物学的认知模型，就其本质而言，规模大，计算要求高。因此，我们打算开发仿真工具和专门的硬件基础设施，以显著提高速度和规模，与世界上最好的产品相媲美。第三，理解和利用适应在一个庞大、复杂、运转良好的系统中所起的关键作用是困难的。挑战来自于确保系统在适应过程中保持稳定，在认知水平上概括任务中的学习行为，以及理解不同领域（例如感知，运动控制和认知）中明显的不同类型适应之间的关系。解决这三大挑战将使我们能够构建下一代“全脑”模型。这项研究将进一步测试一个新的理论框架，将感知，运动和认知行为整合到生物学上合理的模型中。拟议的研究将显着提高SPA模型的适应性和功能，并具有重要的实际后果，将模型更直接地连接到医疗干预措施，可以帮助诊断，预防和修复神经系统损伤。最后，理解自然界为现实世界中的难题找到的独特解决方案，应该为智能系统、模式识别和决策应用中的创新工程解决方案铺平道路。