Optimization of noise-induced resonance mechanismson co-evolutionary neurobiological networks

噪声的功能作用是神经生物学中一个长期存在的研究问题。虽然噪声通常是不期望的，但其共振效应是众所周知的，并且通常被认为是神经元在其信息编码能力方面的正常功能的关键。在单个神经元和神经元网络中都观察到了共振现象。 还已知的是，不同类型的噪声引起的共振机制在不同条件下发生。这些由神经元参数、神经元之间的突触连接、网络拓扑和噪声源的不同组合组成。 所有以前的研究都集中在理解这些噪声引起的共振机制中的每一个的优化：（a）在非自适应神经网络中，以及（B）彼此独立。通过优化（a）协同进化（自适应）神经网络中的这些噪声诱导的共振机制，以及（B）通过使用两个或多个噪声诱导机制之间的相互作用，对信息处理的优化的全面理解仍然完全缺乏。该项目的主要目标是设计和分类的效率，过多的优化方案的三种不同类型的噪声引起的共振机制，在共同进化的生物神经网络。主要研究了协同进化网络、无标度网络、小世界网络、随机网络及其多层网络中的相干共振（CR）、自诱导随机共振（SISR）和递归共振（RR）等噪声诱导共振机制。这些神经网络将由生物物理学Hodgin-Huxley（HH）神经元模型组成，并根据尖峰时间依赖性可塑性学习规则或时间活动依赖性结构可塑性学习规则进化。 在考虑HH神经元/网络之前，将利用几何奇异摄动理论和随机多维反应率理论确定单个HH神经元发生CR和SISR的必要条件，根据网络的拓扑结构、突触特性和学习规则，设计CR、SISR和RR的不同优化方案，并根据其效率进行分类。 这是一个及时而独特的建议，有望将我们对最佳神经编码和信息处理的理解推向新的前沿。

The functional role of noise is a long-standing research question in neurobiology. While noise is generally undesirable, its resonance effect is well known and is generally accepted to be crucial for the proper functioning of neurons in terms of their information coding capabilities. The phenomenon of resonance has been observed both in individual neurons and as well as in networks of neurons. It is also known that different types of noise-induced resonance mechanisms occur under different conditions. These consist of different combinations of neuron parameters, synaptic connections between neurons, network topology, and noise sources. All previous research has been focused on understanding the optimization of each of these noise-induced resonance mechanisms: (a) in non-adaptive neural networks, and (b) independently of one another. A comprehensive understanding of optimization of information processing via the optimization of these noise-induced resonance mechanisms in (a) co-evolutionary (adaptive) neural networks, and (b) by using the interplay between two or more noise-induced mechanisms, is still completely lacking. The main objective of this project is to design and classify in terms of efficiency, a plethora of optimization schemes for three different types of noise-induced resonance mechanisms, in co-evolutionary biological neural networks. The main focus is on the noise-induced resonance mechanisms of coherence resonance (CR), self-induced stochastic resonance (SISR), and recurrence resonance (RR) in co-evolutionary network motifs, scale-free networks, small-world networks, random networks, and their multilayer networks. These neural networks will consist of the biophysical Hodgin-Huxley (HH) neuron model and evolve according to the spiking time-dependent plasticity learning rule or a temporal activity-dependent structural plasticity learning rule. Before considering the HH neurons/networks, the necessary conditions for the occurrence of CR and SISR in an isolated HH neuron will be determined using geometric singular perturbation theory and stochastic multi-dimensional reaction rate theory.Depending on the topology, the synaptic properties, and the learning rule of a network, different optimization schemes for CR, SISR, and RR will design and classified with respect to their efficiency. This is a timely and unique proposal that promises to push our understanding of optimal neural coding and information processing to new frontiers.