The unity of neuroscience: a flat view

Arnon Levy*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This paper offers a novel view of unity in neuroscience. I set out by discussing problems with the classical account of unity-by-reduction, due to Oppenheim and Putnam. That view relies on a strong notion of levels, which has substantial problems. A more recent alternative, the mechanistic “mosaic” view due to Craver, does not have such problems. But I argue that the mosaic ideal of unity is too minimal, and we should, if possible, aspire for more. Relying on a number of recent works in theoretical neuroscience—network motifs, canonical neural computations (CNCs) and design-principles—I then present my alternative: a “flat” view of unity, i.e. one that is not based on levels. Instead, it treats unity as attained via the identification of recurrent explanatory patterns, under which a range of neuroscientific phenomena are subsumed. I develop this view by recourse to a causal conception of explanation, and distinguish it from Kitcher’s view of explanatory unification and related ideas. Such a view of unity is suitably ambitious, I suggest, and has empirical plausibility. It is fit to serve as an appropriate working hypothesis for 21st century neuroscience.

Original languageAmerican English
Pages (from-to)3843-3863
Number of pages21
JournalSynthese
Volume193
Issue number12
DOIs
StatePublished - 1 Dec 2016

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.

Keywords

  • CNC
  • Explanation in neuroscience
  • Levels
  • Mechanistic mosaic
  • Network motifs
  • Oppenheim and Putnam
  • Sparsify
  • Unity of science

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