Neural Computing with Oscillations

几十年来，了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。科学家们正在逐渐剥离这些层面，一个接一个地揭示简单行为背后的神经机制。振荡在大脑中无处不在。了解它们的作用是我们理解大脑一般工作原理的基础。该项目建议研究振荡在神经计算中的作用，重点是导航和定位，即动物对其在太空中所在位置的感知。对内嗅皮层(EC)和海马区(HC)的大量生物学观察表明，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来解释这些区域的振荡如何对这种编码做出贡献。然而，这些模型以一种有点零碎和脱节的方式处理观察结果，并且缺乏一个总体的、简单的框架。通过我们在医学图像处理方面的经验，我们已经看到了傅里叶变换及其性质如何为理解和处理振荡提供了一个理论框架。我们计划应用我们的信号处理知识，并为这些导航过程提出一个傅立叶框架。此外，我们将研究形成这些振荡网络可能涉及的自组织原理。对这个系统中的振荡的原则性研究可能会转化为其他系统和其他振荡。我们的工作可能为更好地理解与振荡相关的神经疾病铺平道路，如癫痫、帕金森氏症、阿尔茨海默氏症、精神分裂症，可能还有ADHD和双相情感障碍。更深入地了解动物大脑中的振荡是如何利用的，也可能导致在智能机器人和脑机接口中使用神经模型方面的突破。

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.