Chaos, multiplicity, crisis, and synchronicity in higher order neural networks
We study a randomly diluted higher-order network of spinlike neurons that interact via Hebbian-type connections and derive and solve exact dynamical equations for a general block-sequential updating algorithm. The system has a variety of static and oscillatory solutions. The bifurcation parameters i...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/94715 http://hdl.handle.net/10220/8128 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | We study a randomly diluted higher-order network of spinlike neurons that interact via Hebbian-type connections and derive and solve exact dynamical equations for a general block-sequential updating algorithm. The system has a variety of static and oscillatory solutions. The bifurcation parameters in the present model include neuronal interaction coefficients, the synchronicity parameter, and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. |
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