Information retrieval by a neural-network system with continuous attractors

具有连续吸引子的神经网络系统的信息检索

基本信息

批准号：
16500190
负责人：
OKAMOTO Hiroshi
金额：
$ 1.92万
依托单位：
RIKEN (2006)富士ゼロックス株式会社研究本部 (2004-2005)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

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

长期以来，人们一直认为，神经网络系统中的信息检索是由具有离散固定点吸引子的动态系统描述的。然而，从神经力学分级持续活性和计算模型的神经生理发现的证据表明，从大脑中的长期记忆中检索短期记忆的回收更可能是由具有固定点吸引子的动力学来描述的，这些动力可以不断地取决于初始状态（例如，持续吸收器动力学）。在心理学中，人们普遍认为，长期记忆在网络结构（例如语义网络）中存档。在这里，我们通过连续的ATTRACTOR动力学从各种现实世界中的复杂网络（www/Internet，人类网络，社交网络，基因/生化反应网络等之间的引用）中提出信息检索，该网络与从长期记忆中的短期记忆中相比，通过连续的ATRATER-ADTRACTOR动力学进行了类似。对于给定的COM…更多的PLEX网络，具有滞后/输出关系的神经元与每个节点相对应，并且与每个链接的突触连接相对应。用户想知道的（即用户的“查询”）以神经元激活模式的初始状态编码。每个神经元假定的滞后特性对于产生稳健的连续吸引子至关重要。作为连续吸引子获得的激活模式代表了查询的“答案”。通过将此信息 - 恢复算法应用于科学文章的引文网络（300,000个神经科学提供了科学引文指数扩展，汤姆森科学，并获得了许可），我们确认，在回应给定的查询时，一组相关的文档得到了充分的提取。例如，对于查询“分级持续活动和神经元积分器”，通过可视化提取的文档和它们之间的引文关系，可以理解哪些文章是主要或附件，哪些文章之间的关系是支流的主流。为了阐明实际神经元是否具有滞后特征，例如在拟议的算法中假设的特征，我们分析了在GO/NO-GO歧视任务期间从猴子扣带皮质中记录的分级活性。我们发现，点火率分布显示出明显的双峰性，这与具有滞后特征的神经元的理论预测一致。还证明，具有滞后特征的神经元的复发网络可以很好地复制实验观察到的分级活性的特征。这些结果表明，所提出的算法的信息检索与大脑长期记忆的短期记忆检索相似。据我们所知，这是从真实大脑推断出非平凡的，实际上有用的信息处理算法的第一个成功示例。较少的