Grant: Discovery Grants Program - Individual

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.