2006 Fiscal Year Final Research Report Summary
Information retrieval by a neural-network system with continuous attractors
| Project/Area Number |
16500190
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | RIKEN (2006)
富士ゼロックス株式会社研究本部 (2004-2005) |
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Principal Investigator |
OKAMOTO Hiroshi RIKEN Brain Science Institute, Neural Circuit Theory, 脳回路機能理論研究チーム, 客員研究員 (00374067)
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| Project Period (FY) |
2004 – 2006
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| Keywords | Graded persistent activity / Continuous attractor / Decision making / Temporal integration / Brain computing / Information retrieval / Complex network / Citation |
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| Research Abstract |
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
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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
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