Large-scale neural models of cognitive function

我的实验室已经开发出了使用生物学上看似合理的尖峰神经元来模拟认知行为的方法，这导致了目前世界上最大的功能大脑模型--Spaun。最新版本的Spaun有超过660万个神经元，200亿个连接，能够执行各种任务。该模型展示了我实验室开发的两个理论，神经工程框架(NEF)和语义指针体系结构(SPA)，它们共同勾勒出了在脉冲神经网络中构建认知模型的一般方法和体系结构，并实现了一种独特的生物认知方法。我们现在遇到的压倒一切的挑战，也是这项提议的重点，是扩大理论和模拟，以在更苛刻的环境中处理认知任务，并提供更多生物学细节。我们确定了我们需要应对的三个主要挑战，以保持我们作为该领域领导者的地位。首先，还没有一个群体在大规模认知模型中使用高度逼真的单神经元模型(例如，隔室模型、基于电导的模型)来捕捉心理功能。这样做有望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、脑深部刺激等)。其次，生物系统非常擅长处理时间和空间上的连续性。目前的理论没有充分处理神经网络如何表示和处理这种连续性。我们将继续开发和利用空间语义指针(SSP)和我们为此目的开发的Legendre存储单元(LMU)。第三，理解和利用适应在大脑这样一个庞大、复杂、起作用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期间保持稳定，并在认知水平上概括各种任务的习得行为。解决这三个广泛的挑战将使我们能够构建下一代“全脑”模型。这项研究将进一步推进这些理论框架，以便在生物学上可信的模型中整合感知、运动和认知行为。建议的研究将显著提高NEF和SPA模型的适应性和功能性，并具有将模型与有助于诊断、预防和修复神经系统损伤的医疗干预相联系的重要实用结果。此外，了解大自然为困难的现实世界问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。

My lab has 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. The most recent version of Spaun has over 6.6 million neurons with 20 billion connections, and is able to perform a wide variety of tasks. This model demonstrates two theories developed in my lab, the Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA); together, these outline a general method and architecture for constructing cognitive models in spiking neural networks, and realize a unique approach to biological cognition. The overriding 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 and with greater biological detail. 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, biological systems are extremely adept at dealing with continuity in time and in space. Current theory does not adequately deal with how neural networks represent and process such continuity. We will continue to develop and exploit Spatial Semantic Pointers (SSPs) and the Legendre Memory Unit (LMU), which we developed for such purposes. Third, understanding and exploiting the critical role that adaptation plays in a large, complex, functioning system like the brain, is difficult. Challenges stem from ensuring that the system remains stable during adaptation and generalizing learned behaviours across tasks at the cognitive level. Addressing these three broad challenges will allow us to construct the next generation of `whole-brain' models. This research will further advance these theoretical frameworks for integrating perceptual, motor, and cognitive behaviour in biologically plausible models. The proposed research will significantly improve the adaptability and functionality of NEF and SPA models, and have the important practical consequence of connecting models to medical interventions that can help with the diagnosis, prevention, and repair of damage to neural systems. In addition, understanding the unique solutions that nature has found for difficult real-world problems will pave the way for innovative engineering solutions in intelligent systems, pattern recognition, and decision-making applications.