How Do Neural Systems Use Probabilistic Inference That Is Context-Sensitive to Create and Preserve Organized Complexity?
| Autor: | Phillips, William |
|---|---|
| Přispěvatelé: | Simeonov, PL, Smith, LS, Ehresmann, AC |
| Rok vydání: | 2012 |
| Předmět: |
Self-organization
Relation (database) Computer science business.industry Reliability (computer networking) context-sensitivity Context (language use) Coherent Infomax Probabilistic inference Machine learning computer.software_genre self-organization neural systems Development (topology) Neural system probabilistic inference Artificial intelligence Infomax complexity business induction computer |
| Zdroj: | Integral Biomathics ISBN: 9783642281105 |
| DOI: | 10.1007/978-3-642-28111-2_7 |
| Popis: | This paper claims that biological systems will more effectively create organized complexity if they use probabilistic inference that is context-sensitive. It argues that neural systems combine local reliability with flexible, holistic, context-sensitivity, and a theory, Coherent Infomax, showing, in principle, how this can be done is outlined. Ways in which that theory needs further development are noted, and its relation to Friston’s theory of free energy reduction is discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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