Neural Computing with Oscillations

具有振荡的神经计算

• 批准号: 298181-2013
• 负责人: Orchard, Jeff
• 金额: $ 1.09万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634963
• 关键词: Neural; Computing; Oscillations

Understanding how intelligent behaviour emerges from a network of neurons has been the holy grail of psychology and neuroscience for decades. Scientists are gradually peeling back the layers, exposing the neurological mechanisms behind simple behaviours, one by one. Oscillations are ubiquitous in the brain. Understanding their role is fundamental in our understanding of how the brain works in general. This project proposes to investigate the role of oscillations in neural computing, with a focus on navigation and localization, an animal's perception of where it is in space. A number of biological observations of the entorhinal cortex (EC) and hippocampus (HC) suggest that they are involved in navigation and localization. Recent computational models have proposed a variety of mechanisms for how oscillations in these regions contribute to this encoding. However, these models address the observations in a somewhat piecemeal and disconnected fashion, and lack an overarching, simple framework. Through our experience in medical image processing, we have seen how the Fourier transform and its properties offer a theoretical framework for understanding and manipulating oscillations. We plan to apply our knowledge of signal processing and propose a Fourier framework for these navigation processes. Moreover, we will investigate what self-organizing principles might be involved in forming these oscillating networks. A principled study of oscillations in this system might translate to other systems and other oscillations. Our work could pave the way to a better understanding of oscillation-related neurological disorders such as epilepsy, Parkinson's, Alzheimer's, schizophrenia, and possibly ADHD and bipolar disorder. A deeper understanding of how oscillations are leveraged in the animal brain could also lead to breakthroughs in the use of neural models for intelligent robotics and brain-machine interfaces.

几十年来，理解智能行为是如何从神经元网络中产生的一直是心理学和神经科学的圣杯。科学家们正在逐步揭开这些层，一个接一个地揭示简单行为背后的神经机制。脑震荡在大脑中无处不在。理解它们的作用是我们理解大脑一般如何工作的基础。该项目旨在研究振荡在神经计算中的作用，重点是导航和定位，即动物对空间位置的感知。对内嗅皮质（EC）和海马（HC）的许多生物学观察表明它们参与导航和定位。最近的计算模型已经提出了各种机制，在这些地区的振荡如何有助于这种编码。然而，这些模型以一种有点零碎和不连贯的方式处理观察结果，缺乏一个总体的、简单的框架。通过我们在医学图像处理方面的经验，我们已经看到傅立叶变换及其属性如何为理解和操纵振荡提供理论框架。我们计划运用我们的信号处理知识，并提出了这些导航过程的傅立叶框架。此外，我们还将研究在形成这些振荡网络时可能涉及哪些自组织原理。对这个系统中振荡的原理性研究可能会转化为其他系统和其他振荡。我们的工作可以为更好地理解与振荡相关的神经系统疾病铺平道路，如癫痫，帕金森氏症，阿尔茨海默氏症，精神分裂症，可能还有多动症和双相情感障碍。更深入地了解动物大脑中的振荡是如何被利用的，也可能导致在智能机器人和脑机接口中使用神经模型的突破。