Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/43686
Título: | Combining spatial and parametric working memory in a dynamic neural field model |
Autor(es): | Wojtak, Weronika Coombes, Stephen Bicho, Estela Erlhagen, Wolfram |
Palavras-chave: | Dynamic neural field Amari model Working memory Neural Integrator |
Data: | Nov-2016 |
Editora: | Springer International Publishing AG |
Revista: | Lecture Notes in Computer Science |
Resumo(s): | We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which supports the existence of localized activity patterns or “bumps” with a continuum of amplitudes. Bump solutions have been used in the past to model spatial working memory. We apply the model to explain input-specific persistent activity that increases monotonically with the time integral of the input (parametric working memory). In numerical simulations of a multi-item memory task, we show that the model robustly memorizes the strength and/or duration of inputs. Moreover, and important for adaptive behavior in dynamic environments, the memory strength can be changed at any time by new behaviorally relevant information. A direct comparison of model behaviors shows that the 2-field model does not suffer the problems of the classical Amari model when the inputs are presented sequentially as opposed to simultaneously. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/43686 |
ISBN: | 9783319447773 |
DOI: | 10.1007/978-3-319-44778-0_48 |
ISSN: | 0302-9743 |
Versão da editora: | www.springer.com |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ICANN2016.pdf | 732,18 kB | Adobe PDF | Ver/Abrir |