Implementing Bayes’ rule with neural fields
Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be i...
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2008
|
| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/10954 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/10954 |
| Resumo: | Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory. |
|---|