Explanatory completeness and idealization in large brain simulations: a mechanistic perspective.
The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: (1) Blue...
| Publicado en: | Synthese Vol. 193; no. 5; pp. 1457 - 1479 |
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| Formato: | Artículo |
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Springer Nature
May2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=115054837&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 115054837 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: May2016 vid: 193 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 115054837 10.1007/s11229-015-0731-3 ppf: 1457 ppct: 22 formats: fmt: @attributes: type: P size: 520KB tig: atl: Explanatory completeness and idealization in large brain simulations: a mechanistic perspective. aug: au: Miłkowski, Marcin affil: Institute of Philosophy and Sociology, Polish Academy of Sciences, ul. Nowy Świat 72 00-330 Warszawa Poland su: Brain Computer simulation Explanation Neurosciences Realism Spatiotemporal processes sug: subj: Brain Computer simulation Explanation Neurosciences Realism Spatiotemporal processes keyword: Blue brain Bonini's paradox Human brain project Idealization Mechanistic explanation SPAUN ab: The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: (1) Blue Brain, used for particular simulations of the cortical column in hybrid models, and (2) Eliasmith's SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a 'checklist' for a modeler. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2016. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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