Information retrieval by a neural-network system with continuous attractors

长期以来，人们一直假设神经网络系统中的信息检索是由具有离散不动点吸引子的动力系统描述的。然而，从神经生理学的证据发现分级的持续活动和计算建模的神经机制表明，检索短期记忆的长期记忆在大脑中更可能是描述的动力学与固定点吸引子，持续依赖于初始状态（即连续吸引子动力学）。在心理学中，长期记忆通常被认为是以网络结构（例如语义网络）存档的。在这里，我们提出了从各种现实世界的复杂网络（WWW/互联网，科学文章之间的引用，人类网络，社会网络，基因/生化反应网络等）的信息检索。通过连续吸引子动力学，类比于从一个大的长期记忆网络中恢复短期记忆。对于给定的com 关于我们 plex网络中，每个节点对应一个具有滞后输入输出关系的神经元，每个链路对应一个突触连接。用户想要知道的（即用户的“查询”）被编码在神经元的激活模式的初始状态中。假设为每个神经元的滞后特性是必不可少的产生强大的连续吸引子。作为连续吸引子获得的激活模式表示对查询的“答案”。通过将这种信息检索算法应用于科学文章的引用网络（300，000神经科学提供的Science Citation Index Expanded，Thomson Scientific，经许可），我们证实，在响应给定查询时，一组相关文档被充分提取。例如，对于查询“分级持续活动和神经集成器”，通过可视化提取的文档和它们之间的引用关系，可以理解哪些文章是主要的或附属的，以及哪些文章之间的关系是主流或支流。为了阐明真实的神经元是否具有滞后特性，例如在所提出的算法中假设的那些，我们分析了在Go/No-go辨别任务期间从猴子扣带皮层记录的分级活动。我们发现，放电率分布显示出明显的双峰性，这是与神经元的滞后特性的理论预测相一致。它还表明，具有滞后特性的神经元的递归网络可以很好地复制实验观察到的分级活动的功能。这些结果表明，所提出的算法的信息检索是非常类似的短期记忆检索在大脑中的长期记忆。据我们所知，这是第一个从真实的大脑中推断出非平凡、实用的信息处理算法的成功例子。少

It has long been hypothesized that information retrieval in neural-network systems is described by dynamical systems with discrete fixed-point attractors. However, evidence from neurophysiological findings of graded persistent activity and computational modeling of its neural mechanisms suggests that retrieval of short-term memory from long-term memory in the brain is more likely to be described by dynamics with fixed-point attractors that continuously depend on the initial state (say, continuous-attractor dynamics). In psychology, it has been generally considered that long-term memory is archived in a network structure (e.g. semantic network). Here we propose information retrieval from a variety of real-world complex networks (WWW/internet, citation between scientific articles, human network, social network, gene/biochemical-reaction network, etc.) by continuous-attractor dynamics, by analogy with retrieval of short-term memory from a large network of long-term memory. For a given com … More plex network, a neuron with hysteretic input/output relation corresponds with each node, and a synaptic connection with each link. What a user wants to know (i.e. user's "query") is encoded in an initial state of the activation pattern of the neurons. The hysteretic characteristics assumed for each neuron are essential for producing robust continuous attractors. An activation pattern obtained as a continuous attractor represents an "answer" to the query. By applying this information-retrieval algorithm to a citation network of scientific articles (300,000 neuroscience provided Science Citation Index Expanded, Thomson Scientific, with permission), we confirmed that, in response to a given query, a set of relevant documents were adequately extracted. For instance, for a query "graded persistent activity and neural integrator", by visualizing the extracted documents and citation relations between them, one can comprehend which articles are principal or accessory and which relations between articles are mainstreams of tributaries. To elucidate whether real neurons have hysteretic characteristics such as those hypothesized in the proposed algorithm, we analyzed graded activity recorded from the monkey cingulate cortex during Go/No-go discrimination task. We found that the firing-rate distribution shows clear bimodality, which is consistent with the theoretical prediction for neurons with hysteretic characteristics. It was also demonstrated that a recurrent network of neurons with hysteretic characteristics could well replicate experimentally observed features of graded activity. These results suggest that information retrieval by the proposed algorithm is quite analogous to short-term memory retrieval from long-term memory in the brain. To our knowledge, this is the first successful example to infer non-trivial, practically useful information-processing algorithm from real brain. Less