基于最优二值信号检测理论的神经网络装配记忆模型
A Neural Network Assembly Memory Model Based on an Optimal Binary Signal Detection Theory
摘要：
描述了三进制/二进制数据编码算法和Hopfield网络实现最佳卷积或汉明解码算法的条件。采用最优二进制信号检测理论（BSDT）的编解码方法，建立了一个神经网络装配记忆模型（NAMM）。该模型提供了最佳的（最好的）基本存储器性能，并要求使用一个新的存储器单元结构与两层Hopfield网络，N通道时间门，辅助参考存储器，和两个嵌套的反馈回路。NAMM明确地描述了记忆痕迹提取对时间的依赖性，给出了元记忆模拟、广义知识表示以及有意识和无意识心理过程的不同描述的可能性。一个最小的不可分割的部分或“原子”的意识模型也被定义。的NNAMM的神经生物学背景和它的应用，以解决一些跨学科的问题进行了简短的讨论。BSDT可以实现动物和人类神经组织中使用的“最佳神经代码”。
英文摘要：
A ternary/binary data coding algorithm and conditions under which Hopfield networks implement optimal convolutional or Hamming decoding algorithms has been described. Using the coding/decoding approach (an optimal Binary Signal Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model (NNAMM) is built. The model provides optimal (the best) basic memory performance and demands the use of a new memory unit architecture with two-layer Hopfield network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. NNAMM explicitly describes the dependence on time of a memory trace retrieval, gives a possibility of metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. A model of smallest inseparable part or an "atom" of consciousness is also defined. The NNAMM's neurobiological backgrounds and its applications to solving some interdisciplinary problems are shortly discussed. BSDT could implement the "best neural code" used in nervous tissues of animals and humans.
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