Functional networks from inverse modeling of neural population activity

Simona Cocco, Rémi Monasson, Lorenzo Posani, Gaia Tavoni

Research output: Contribution to journalReview articlepeer-review

10 Scopus citations

Abstract

The availability of large-scale neural multi-electrode or optical recordings make now possible the modelling of the simultaneous activities of tens to thousand of neurons. One promising approach relies on the inference of detailed functional connectivity between the recorded cells, that is, of an effective coupling network reproducing the correlation structure of the spiking events. Here we report some recent applications of those approaches to retinal, hippocampal, and cortical data, illustrating in particular how functional coupling networks may be useful to decode complex brain representations, and how their changes may be tracked in behaving animals, with a possible connection to behavioral learning. Statistical, theoretical, and neurobiological issues raised by the inverse modeling of population activity are discussed.

Original languageEnglish
Pages (from-to)103-110
Number of pages8
JournalCurrent Opinion in Systems Biology
Volume3
DOIs
StatePublished - Jun 2017

Keywords

  • Cell assemblies
  • Functional connectivity
  • Hippocampus
  • Inference
  • Ising model
  • Map decoding
  • Memory replay
  • Prefrontal cortex
  • Retina

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