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FOCI
2007
IEEE

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

10 years 9 months ago
Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency
Recent progress in genomics and proteomics makes it possible to understand the biological networks at the systems level. We aim to develop computational models of learning and memory inspired by the biomolecular networks embedded in their environment. One fundamental question is how the systems rapidly adapt to their changing environment in a short period (learning) while performing persistently through the longer time span (memory). We study this issue in a probabilistic hypergraph model called the hypernetworks. The hypernetwork architecture consists of a huge number of randomly sampled hyperedges, each corresponding to higherorder micromodules in the input. We find that a system consisting of a large number of a wide range of heterogeneous low-dimensional components has a fairly competitive chance of long-term survival (memory, persistency) and short-term performance (learning, adaptability) as opposed to a system consisting of a small number of high-dimensional, fine-tuned, complex...
Byoung-Tak Zhang
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where FOCI
Authors Byoung-Tak Zhang
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