带索引检索的非标准记忆模型
Non-standard memory models with indexed retrieval
AI总结：
本研究提出源于生物学观察的非标准记忆模型，基于以神经元为中心的假说实现一次性学习，可通过索引检索恢复完整模式，用于MNIST模式分类。
AI中文摘要：
神经网络的标准记忆模型是Hopfield网络的变体，其中特征表示以向量形式存储在矩阵中。检索基于输入向量与存储向量集合的相似度，以内容寻址方式进行，使网络向最近的存储吸引子演化。换句话说，Hopfield式神经网络模型（“联想记忆”）失去了通过索引直接寻址记忆中项目的有用特性。在本扩展摘要中，我们提出了一种新模型，该模型极其简单，源于生物学观察，却带来了显著的概念进步和技术优势。目标是基于以神经元为中心的假说建立适应性：可塑性由调节自身突触的神经元组织。据此，我们实现了一种局部主义的、以神经元为中心的一次性学习方法，并将其应用于简单的模式分类问题（MNIST）。我们寻找高信息神经元的存在，作为表示的索引，其理念是我们能够通过索引检索恢复完整模式，而非从吸引子进行联想向量检索。
英文摘要：
The standard memory models for neural networks are variants of the Hopfield network, where feature representations are stored as vectors in a matrix. Retrieval happens based on similarity between an input vector and the set of stored vectors in a content-addressable manner such that the network evolves towards the closest stored attractor. In other words, the useful property of addressing items in memory directly by index is lost in Hopfield-style neural network models ("associative memory"). In this extended abstract, we present a new model which is extremely simple, derived from biological observation, yet introduces a significant conceptual advance and technical benefits. The goal is to establish adaptivity based on the neuron-centric hypothesis: Plasticity is organized by the neuron which regulates its own synapses. Accordingly we implemented a localist, neuron-centric one-shot learning method and applied it to a simple pattern classification problem (MNIST). We were looking for the existence of high information neurons, to act as indices into the representations. The idea was that we would be able to restore full patterns by indexed retrieval, instead of associative vector retrieval from attractors.
