---
res:
  bibo_abstract:
  - Normative theories and statistical inference provide complementary approaches
    for the study of biological systems. A normative theory postulates that organisms
    have adapted to efficiently solve essential tasks, and proceeds to mathematically
    work out testable consequences of such optimality; parameters that maximize the
    hypothesized organismal function can be derived ab initio, without reference to
    experimental data. In contrast, statistical inference focuses on efficient utilization
    of data to learn model parameters, without reference to any a priori notion of
    biological function, utility, or fitness. Traditionally, these two approaches
    were developed independently and applied separately. Here we unify them in a coherent
    Bayesian framework that embeds a normative theory into a family of maximum-entropy
    “optimization priors.” This family defines a smooth interpolation between a data-rich
    inference regime (characteristic of “bottom-up” statistical models), and a data-limited
    ab inito prediction regime (characteristic of “top-down” normative theory). We
    demonstrate the applicability of our framework using data from the visual cortex,
    and argue that the flexibility it affords is essential to address a number of
    fundamental challenges relating to inference and prediction in complex, high-dimensional
    biological problems.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Wiktor F
      foaf_name: Mlynarski, Wiktor F
      foaf_surname: Mlynarski
      foaf_workInfoHomepage: http://www.librecat.org/personId=358A453A-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Michal
      foaf_name: Hledik, Michal
      foaf_surname: Hledik
      foaf_workInfoHomepage: http://www.librecat.org/personId=4171253A-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Thomas R
      foaf_name: Sokolowski, Thomas R
      foaf_surname: Sokolowski
      foaf_workInfoHomepage: http://www.librecat.org/personId=3E999752-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-1287-3779
  - foaf_Person:
      foaf_givenName: Gašper
      foaf_name: Tkačik, Gašper
      foaf_surname: Tkačik
      foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-6699-1455
  bibo_doi: 10.1016/j.neuron.2021.01.020
  bibo_issue: '7'
  bibo_volume: 109
  dct_date: 2021^xs_gYear
  dct_identifier:
  - UT:000637809600006
  dct_language: eng
  dct_publisher: Cell Press@
  dct_title: Statistical analysis and optimality of neural systems@
  fabio_hasPubmedId: '33592180'
...