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Inhomogeneous Sparseness Leads to Dynamic Instability During Sequence Memory Recall in a Recurrent Neural Network Model
Theoretical models of associative memory generally assume most of their parameters to be homogeneous across the network. Conversely, biological neural networks exhibit high variability of structural as well as activity parameters. In this paper, we extend the classical clipped learning rule by Wills...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer
2013
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3844438/ https://www.ncbi.nlm.nih.gov/pubmed/23876197 http://dx.doi.org/10.1186/2190-8567-3-8 |