RIA: Dynamics of Oscillatory Neural Systems

这个项目探索下一代神经计算的基础上 神经元柱的振荡行为， 哺乳动物的皮层和嗅球。 的生物学基础 比信号的幅度看得更远就足够了， 支持最近的发现，如振荡的证据， 兔子的嗅球，帮助他们确定 其他兔子的性别和年龄。 这种状态的神经元不会 被认为是一个简单的水平或数量级，而是一个组合 幅度，频率，相位，也许还有其他属性， 其输出相对于其他相邻神经元。 本研究 旨在分析和表征耦合的行为， 用数学方法导出闭合形式的振荡系统 动力学方程的求解和仿真， 耦合振荡柱可以通过改变 激励、自反馈增益、横向耦合和频率 振荡参数 研究了高斯噪声对大 振荡柱的网络也将被研究。

This project explores next-generation neural computing based on oscillatory behavior of neuron columns found in the visual cortex and olfactory bulbs of mammals. The biological basis for looking further than magnitude of signals is sufficient as supported by recent discoveries such as evidence of oscillations in the olfactory bulbs of rabbits which helped them to determine the sex and age of other rabbits. That state of neuron would not be considered as a simple level or magnitude, but rather a combination of magnitude, frequency, phase, and perhaps other attributes, of its output relative to other neighboring neurons. This research seeks to analyze and characterize the behavior of coupled oscillatory systems by mathematically deriving closed-form solutions to the dynamic equations and simulation, the behavior of the coupled oscillatory columns can be observed by varying the stimulus, self-feedback gain, lateral coupling, and frequency parameter of oscillation. The effects of Gaussian noise on large networks of oscillatory columns will also be investigated.