具有最大似然召回和识别特性的神经网络装配记忆模型
A Neural Network Assembly Memory Model with Maximum-Likelihood Recall and Recognition Properties
摘要：
它已被证明，最近提出的一个神经网络模型来描述基本的记忆性能是基于一个三进制/二进制编码/解码算法，这导致了一个新的神经网络组装记忆模型（NAMM）提供最大似然召回/识别性能，并意味着一个新的记忆单元结构与Hopfield双层网络，N通道时间门，辅助参考记忆，和两个嵌套的反馈回路。对于所使用的数据编码，条件下发现的一个版本的Hopfied网络实现最大似然卷积解码算法，同时，线性统计分类器的任意二进制向量相对于汉明距离之间的矢量分析和参考矢量给定。除了基本的记忆表现等外，该模型还明确地描述了记忆痕迹提取的时间依赖性，为一次性学习、元记忆模拟、广义知识表示以及区分有意识和无意识心理过程提供了可能。它已经表明，一个组装记忆单元可以被看作是一个最小的不可分割的部分或意识的“原子”的模型。讨论了一些非传统的神经生物学背景（动态时空同步、时间依赖和错误检测神经元的性质、早期精确锋电位发放等）以及该模型在解决不同科学领域交叉问题中的应用。
英文摘要：
It has been shown that a neural network model recently proposed to describe basic memory performance is based on a ternary/binary coding/decoding algorithm which leads to a new neural network assembly memory model (NNAMM) providing maximum-likelihood recall/recognition properties and implying a new memory unit architecture with Hopfield two-layer network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. For the data coding used, conditions are found under which a version of Hopfied network implements maximum-likelihood convolutional decoding algorithm and, simultaneously, linear statistical classifier of arbitrary binary vectors with respect to Hamming distance between vector analyzed and reference vector given. In addition to basic memory performance and etc, the model explicitly describes the dependence on time of memory trace retrieval, gives a possibility of one-trial learning, metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. It has been shown that an assembly memory unit may be viewed as a model of a smallest inseparable part or an 'atom' of consciousness. Some nontraditional neurobiological backgrounds (dynamic spatiotemporal synchrony, properties of time dependent and error detector neurons, early precise spike firing, etc) and the model's application to solve some interdisciplinary problems from different scientific fields are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
