{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T05:03:25Z","timestamp":1772427805152,"version":"3.50.1"},"reference-count":45,"publisher":"MIT Press - Journals","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p> It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaotic irregular firing in the IO neuron assembly; such chaotic resonance (CR) was replicated in their computer demonstration of a Hodgkin-Huxley (HH)-type compartment model. In this study, we examined the response of CR to a periodic signal in the IO neuron assembly comprising the Llin\u00e1s approach IO neuron model. This system involves empirically observed dynamics of the IO membrane potential and is simpler than the HH-type compartment model. We then clarified its dependence on electrical coupling strength, input signal strength, and frequency. Furthermore, we compared the physiological validity for IO neurons such as low firing rate and sustaining subthreshold oscillation between CR and conventional stochastic resonance (SR) and examined the consistency with asynchronous firings indicated by the previous model-based studies in the cerebellar learning process. In addition, the signal response of CR and SR was investigated in a large neuron assembly. As the result, we confirmed that CR was consistent with the above IO neuron\u2019s characteristics, but it was not as easy for SR. <\/jats:p>","DOI":"10.1162\/neco_a_00894","type":"journal-article","created":{"date-parts":[[2016,9,14]],"date-time":"2016-09-14T22:08:17Z","timestamp":1473890897000},"page":"2505-2532","source":"Crossref","is-referenced-by-count":30,"title":["Chaotic Resonance in Coupled Inferior Olive Neurons with the Llin\u00e1s Approach Neuron Model"],"prefix":"10.1162","volume":"28","author":[{"given":"Sou","family":"Nobukawa","sequence":"first","affiliation":[{"name":"Department of Management Information Science, Fukui University of Technology, Fukui, Fukui, 910\u20138505 Japan"}]},{"given":"Haruhiko","family":"Nishimura","sequence":"additional","affiliation":[{"name":"Graduate School of Applied Informatics, University of Hyogo, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650\u20138588 Japan"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/0025-5564(71)90051-4"},{"key":"B2","author":"Anishchenko V. 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