认知老化：赫布式学习与批判性的相互作用
Cognitive Aging as Interplay between Hebbian Learning and Criticality
摘要：
认知老化似乎是一个全球退化的故事。随着年龄的增长，会发生许多物理、化学和生物学上的变化。因此，我们可以合理地假设大脑也不例外。这个项目的主要目的是使用神经动力学和学习模型的基础上的自我组织临界性的基本原则，占年龄相关的认知效果。在这方面，神经网络中的学习可以作为在早期发展阶段获得技能和知识的模型，即网络中的老化过程和临界状态作为认知能力的最佳状态。神经网络老化的可能机制是连接和神经元的丢失，噪声水平的增加，白质的减少或更有趣的是更长的学习历史以及几个优化目标之间的竞争。在本文中，我们主要感兴趣的是较长的学习历史对记忆的影响，从而在大脑中的优化。因此，假设以联想记忆模式的形式延长学习可以破坏网络中的临界状态。我们的模型基于Tsodyks和Markrams [49]的动态突触模型，在此过程中探索了标准Hebbian学习与自组织临界现象相结合的效果。该项目主要包括评估和模拟综合神经元和火神经元的网络，这些神经元受到神经元一级老化效应的各种组合，目的是建立初步假设，并利用其重要特征之一，即较长的学习历史，了解认知能力因老化而下降。
英文摘要：
Cognitive ageing seems to be a story of global degradation. As one ages there are a number of physical, chemical and biological changes that take place. Therefore it is logical to assume that the brain is no exception to this phenomenon. The principle purpose of this project is to use models of neural dynamics and learning based on the underlying principle of self-organised criticality, to account for the age related cognitive effects. In this regard learning in neural networks can serve as a model for the acquisition of skills and knowledge in early development stages i.e. the ageing process and criticality in the network serves as the optimum state of cognitive abilities. Possible candidate mechanisms for ageing in a neural network are loss of connectivity and neurons, increase in the level of noise, reduction in white matter or more interestingly longer learning history and the competition among several optimization objectives. In this paper we are primarily interested in the affect of the longer learning history on memory and thus the optimality in the brain. Hence it is hypothesized that prolonged learning in the form of associative memory patterns can destroy the state of criticality in the network. We base our model on Tsodyks and Markrams [49] model of dynamic synapses, in the process to explore the effect of combining standard Hebbian learning with the phenomenon of Self-organised criticality. The project mainly consists of evaluations and simulations of networks of integrate and fire-neurons that have been subjected to various combinations of neural-level ageing effects, with the aim of establishing the primary hypothesis and understanding the decline of cognitive abilities due to ageing, using one of its important characteristics, a longer learning history.
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