Large-scale neural models of cognitive function

***我们已经开发了使用生物学上合理的尖峰神经元模拟认知行为的方法，这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 et al., 2012）。该模型是最近出版的《如何构建大脑》（Eliasmith, 2013）一书中详细描述的工作成果，该书概述了构建这种模型的一般方法和架构，称为语义指针架构（SPA）。我们现在遇到的最重要的挑战，也是本提案的重点，是扩大理论和模拟的规模，以解决更苛刻环境下的认知任务。******我们的中心目标是建立生物学上详细的认知模型。我们已经确定了需要解决的三个主要挑战，以保持我们在该领域的领导地位。首先，在捕捉心理功能的同时，还没有一个小组在大规模认知模型中使用高度逼真的单神经元模型（例如，隔间模型、基于电导的模型）。这样做为更好地描述生物和心理特性之间的关系提供了希望，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，以实现显著的速度和规模改进，与世界上最好的相媲美。第三，理解和利用适应在一个庞大、复杂、运作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，在认知水平上概括跨任务的学习行为，以及理解不同领域（如感知、运动控制和认知）中不同类型的适应之间的关系。解决这三个广泛的挑战将使我们能够构建下一代的“全脑”模型。******这项研究将进一步测试一个新的理论框架，将知觉、运动和认知行为整合到生物学上合理的模型中。本研究将显著提高SPA模型的适应性和功能性，并对更直接地将模型与医学干预联系起来，帮助神经系统损伤的诊断、预防和修复具有重要的实际意义。最后，理解自然界为现实世界难题找到的独特解决方案，将为智能系统、模式识别和决策应用中的创新工程解决方案铺平道路。

***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.