大脑网络的模块化Latching动力模型研究
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摘要
自然是灵感的源泉。人工智能的发展史就是不断向自然智能学习的过程。人们在探索大脑认知机制的道路上永不止息。在人类历史漫长的进化过程中，大脑皮层经历了加速变厚进而层叠、表面积和体积加速增加的过程。5万年前，人猿揖首话别。从形态和结构上，人类和其同宗近亲并不存在本质的差别。但是人类却具有其它任何物种都不具有的、目前为止自然界出现的最高形式的智能——语言。人类语言能力的优势在于能从有限的元素构造出无穷的语句。而将其模型化则是对语言进行深入研究的首要任务。
Latching (锁连)是SISSA (意大利国际高等研究生院)的A. Treves根据语言无穷递归的思想，利用Potts网络构造出来的一种自适应迭代动力学模型。当网络满足一定的条件时，Latching能利用有限的模式生成无穷的模式序列。尽管Latching动力很好的解释了思维、行动、语言中存在的策略转移现象，但也面临着一些最基本的挑战。比如，Latching动力是建立在单个网络上的，而我们熟知的大脑皮层存在很多功能分区。如何结合大脑的连接结构去构造更加符合大脑皮层的Latching动力学模型呢？本文成功构造了模块化Latching动力学模型，并研究了不同的网络结构对Latching序列的影响。作为对模块化Latching动力的支持，本文还研究了听觉模态下的功能子网络结构及其实现机制。具体内容包括以下研究内容：
首先，论文探讨了Potts模型的离散状态在大脑中的可能存在机制。通过对微柱神经元和连接结构的梳理，搭建了微柱的神经元网络。并用Izhikevich神经元模型对网络实现了仿真。仿真的结果表明，微柱的Potts态可能源于神经元子集的同步放电。
其次，在正式开始对Latching动力进行模块化扩展之前，首先利用开源的fMRI数据对听觉皮层的子网络进行了分析。通过单步Mean-Shift转移对fMRI的时序信号进行聚类分析，发现在听觉皮层中，无论是基本级的激活区域，还是比较抽象的高级激活区域，功能子网络都呈现模块化无标度分布。
本文的核心工作就是利用听觉皮层功能子网络的特点对Latching作模块化扩展。本文率先设计并实现了Potts网络中的异联想突触连接公式，将Latching动力推广到模块化网络。并引入突触传输时延和自适应阈值反馈控制使得系统看起来更加符合大脑中的神经传导机制。作为引入模块化Latching动力学模型的载体，分析了大脑网络(M-网络)中Latching转移的特点。
随后，又比较了大脑网络和小脑网络(K-网络)中Latching动力学的差异。研究显示，小脑网络中的Latching链具有相对固定的转移路径，大脑网络则更加可变。小脑网络中的Latching链长度对网络的重绕概率相对稳定，而大脑网络对重绕概率非常敏感。此外，噪声连接和反馈连接对大脑网络的影响较小，而小脑网络则比较敏感。这些结论提示网络的结构可能是促成人类智能出现的原因之一。
最后，论文还探讨功能网络切换的可能机制。当刺激出现时大脑中将存在定向快速Latching动力，那么是什么样的机制促成了网络的快速切换呢？通过对听觉实验的开源fMRI实验的分析发现，网络可能存在着层次性的激活机制，某些核心体素在网络的快切换中可能扮演关键的角色。
Inspiration always stems from nature. The development history of artifcialintelligence just witnesses the process of human learning from the natural intelli-gence. And it seems such a kind of pursuit will never stop in the future. In the longriver of human evolution, the cortex grows thicker, and further into lamination. Theincreasing speed of cortical surface and volume have being accelerated in the pastten thousands of years. About50thousands years ago, the humankind departedfrom their brothers, apes. Though there are no qualitative diferent traits that candiferentiate humankind from their clan relatives, but the language ability, whichis considered to be the most outstanding intelligence in the nature, is amazinglyunique for the humankind. Human language is able to construct infnite sentencesfrom fnite gradients. And how to model such an ability is the frst task towards thefuture studies on languages.
Latching chain, designed by Alessandro Treves from SISSA using Potts net-work, is a kind of adaptive dynamics model that derived from the idea of infniterecursion of language. Latching dynamics generates infnite patterns sequences fromfnite pattern set when some specifc network connection conditions are fulflled.Though latching chain well explains the strategy transition phenomenon in actions,thoughts and language, they also face many basic challenges. For example, latchingchain is built on a single network, which is contrary to the imaging evidences onfunctional area divisions. How to construct a more feasible latching dynamics modelaccording to the cortical network connections? In this thesis, a modular latchingmodel is successfully constructed. And the impacts of diferent network structuresto the modular latching chain are also studied. As a support for the modular latch-ing dynamics, the functional networks of auditory modality and the fast-rewiringmechanism underlying the object-cognition are also investigated by analyzing theopen fMRI dataset. The details are described as follows:
Firstly, as an efort to help others understand the Potts network, the possibleelectrophysiological mechanism of the discrete states of Potts unit is discussed bynetwork simulation, where the neuron characters and network structure are synthe-sized and constructed from the minicolumn. Simulation results using Izhikevich neuronal model shows that the Potts state may stem from the synchronous spikingof neuronal subsets.
Secondly, before the modular extension of Latching dynamics, the function-al network structure of auditory modality is studied by analyzing the open fMRIdataset. One-step mean-shift clustering algorithm is proposed to cluster the fMRItemporal signal. Results show that the functional subnetworks in both the basic-level activation and the subordinate-level activation follow the modular scale-freedistribution.
Then it comes the central part of the thesis, the modular extension of latchingdynamics. Taking the modular small-world network structure into consideration,we successfully design the weight formula of the hetero-associative connection. Theintroduction of transmission delay and adaptive threshold as a global feedback makesthe system more similar to the actual neuronal information processing mechanism.As a carrier supporting the modular latching dynamics, the dynamics features ofcerebrum (M-network) are studied by computation simulations.
After the introduction of modular latching chain, the dynamics diferences be-tween cerebrum and cerebellum (K-network) are compared. Simulation studies showthat the latching chains in the K-network have relative more fxed transition path-way, while they are more fexible in the cerebrum. The length of latching chainsof M-network is robust to the changes of rewiring probability. On contrary, on-ly a small range of rewiring probability supports longer latching chains. Besidesthat, M-network is more robust to the noise pattern pairs and feedback connectionsthant K-nework. These result hints that the specifc connection structure maybeone important factor leading to the advent of human cognition.
Lastly, we discuss the probable switching mechanism underlying the fast cogni-tion to the complex stimulus. There would be directed and fast latching-switchingwhen outer stimuli enters. So what mechanism support the fast-rewiring of func-tional network in the brain? Through the open fMRI dataset analysis, we concludethat there may be hierarchical activation mechanism. That is, fast-rewiring maydepends on some skeleton voxels.
Latching (锁连)是SISSA (意大利国际高等研究生院)的A. Treves根据语言无穷递归的思想，利用Potts网络构造出来的一种自适应迭代动力学模型。当网络满足一定的条件时，Latching能利用有限的模式生成无穷的模式序列。尽管Latching动力很好的解释了思维、行动、语言中存在的策略转移现象，但也面临着一些最基本的挑战。比如，Latching动力是建立在单个网络上的，而我们熟知的大脑皮层存在很多功能分区。如何结合大脑的连接结构去构造更加符合大脑皮层的Latching动力学模型呢？本文成功构造了模块化Latching动力学模型，并研究了不同的网络结构对Latching序列的影响。作为对模块化Latching动力的支持，本文还研究了听觉模态下的功能子网络结构及其实现机制。具体内容包括以下研究内容：
首先，论文探讨了Potts模型的离散状态在大脑中的可能存在机制。通过对微柱神经元和连接结构的梳理，搭建了微柱的神经元网络。并用Izhikevich神经元模型对网络实现了仿真。仿真的结果表明，微柱的Potts态可能源于神经元子集的同步放电。
其次，在正式开始对Latching动力进行模块化扩展之前，首先利用开源的fMRI数据对听觉皮层的子网络进行了分析。通过单步Mean-Shift转移对fMRI的时序信号进行聚类分析，发现在听觉皮层中，无论是基本级的激活区域，还是比较抽象的高级激活区域，功能子网络都呈现模块化无标度分布。
本文的核心工作就是利用听觉皮层功能子网络的特点对Latching作模块化扩展。本文率先设计并实现了Potts网络中的异联想突触连接公式，将Latching动力推广到模块化网络。并引入突触传输时延和自适应阈值反馈控制使得系统看起来更加符合大脑中的神经传导机制。作为引入模块化Latching动力学模型的载体，分析了大脑网络(M-网络)中Latching转移的特点。
随后，又比较了大脑网络和小脑网络(K-网络)中Latching动力学的差异。研究显示，小脑网络中的Latching链具有相对固定的转移路径，大脑网络则更加可变。小脑网络中的Latching链长度对网络的重绕概率相对稳定，而大脑网络对重绕概率非常敏感。此外，噪声连接和反馈连接对大脑网络的影响较小，而小脑网络则比较敏感。这些结论提示网络的结构可能是促成人类智能出现的原因之一。
最后，论文还探讨功能网络切换的可能机制。当刺激出现时大脑中将存在定向快速Latching动力，那么是什么样的机制促成了网络的快速切换呢？通过对听觉实验的开源fMRI实验的分析发现，网络可能存在着层次性的激活机制，某些核心体素在网络的快切换中可能扮演关键的角色。
Inspiration always stems from nature. The development history of artifcialintelligence just witnesses the process of human learning from the natural intelli-gence. And it seems such a kind of pursuit will never stop in the future. In the longriver of human evolution, the cortex grows thicker, and further into lamination. Theincreasing speed of cortical surface and volume have being accelerated in the pastten thousands of years. About50thousands years ago, the humankind departedfrom their brothers, apes. Though there are no qualitative diferent traits that candiferentiate humankind from their clan relatives, but the language ability, whichis considered to be the most outstanding intelligence in the nature, is amazinglyunique for the humankind. Human language is able to construct infnite sentencesfrom fnite gradients. And how to model such an ability is the frst task towards thefuture studies on languages.
Latching chain, designed by Alessandro Treves from SISSA using Potts net-work, is a kind of adaptive dynamics model that derived from the idea of infniterecursion of language. Latching dynamics generates infnite patterns sequences fromfnite pattern set when some specifc network connection conditions are fulflled.Though latching chain well explains the strategy transition phenomenon in actions,thoughts and language, they also face many basic challenges. For example, latchingchain is built on a single network, which is contrary to the imaging evidences onfunctional area divisions. How to construct a more feasible latching dynamics modelaccording to the cortical network connections? In this thesis, a modular latchingmodel is successfully constructed. And the impacts of diferent network structuresto the modular latching chain are also studied. As a support for the modular latch-ing dynamics, the functional networks of auditory modality and the fast-rewiringmechanism underlying the object-cognition are also investigated by analyzing theopen fMRI dataset. The details are described as follows:
Firstly, as an efort to help others understand the Potts network, the possibleelectrophysiological mechanism of the discrete states of Potts unit is discussed bynetwork simulation, where the neuron characters and network structure are synthe-sized and constructed from the minicolumn. Simulation results using Izhikevich neuronal model shows that the Potts state may stem from the synchronous spikingof neuronal subsets.
Secondly, before the modular extension of Latching dynamics, the function-al network structure of auditory modality is studied by analyzing the open fMRIdataset. One-step mean-shift clustering algorithm is proposed to cluster the fMRItemporal signal. Results show that the functional subnetworks in both the basic-level activation and the subordinate-level activation follow the modular scale-freedistribution.
Then it comes the central part of the thesis, the modular extension of latchingdynamics. Taking the modular small-world network structure into consideration,we successfully design the weight formula of the hetero-associative connection. Theintroduction of transmission delay and adaptive threshold as a global feedback makesthe system more similar to the actual neuronal information processing mechanism.As a carrier supporting the modular latching dynamics, the dynamics features ofcerebrum (M-network) are studied by computation simulations.
After the introduction of modular latching chain, the dynamics diferences be-tween cerebrum and cerebellum (K-network) are compared. Simulation studies showthat the latching chains in the K-network have relative more fxed transition path-way, while they are more fexible in the cerebrum. The length of latching chainsof M-network is robust to the changes of rewiring probability. On contrary, on-ly a small range of rewiring probability supports longer latching chains. Besidesthat, M-network is more robust to the noise pattern pairs and feedback connectionsthant K-nework. These result hints that the specifc connection structure maybeone important factor leading to the advent of human cognition.
Lastly, we discuss the probable switching mechanism underlying the fast cogni-tion to the complex stimulus. There would be directed and fast latching-switchingwhen outer stimuli enters. So what mechanism support the fast-rewiring of func-tional network in the brain? Through the open fMRI dataset analysis, we concludethat there may be hierarchical activation mechanism. That is, fast-rewiring maydepends on some skeleton voxels.
引文
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