Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Vortrag

Correlations and signatures of criticality in neural population models

MPG-Autoren
/persons/resource/persons84066

Macke,  J
Center of Advanced European Studies and Research (caesar), Max Planck Society;

Externe Ressourcen

Link
(beliebiger Volltext)

Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Macke, J. (2017). Correlations and signatures of criticality in neural population models. Talk presented at Double Feature Workshop: Bernstein Symposium Kick-Off Symposium. Tübingen, Germany.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-C612-C
Zusammenfassung
Large-scale recording methods make it possible to measure the statistics of neural population activity and to gain insights into the principles that govern the collective activity of neural ensembles. One hypothesis that has emerged from this approach is that neural populations are poised at a thermodynamic critical point. Support for this notion has come from a recent series of studies which identified signatures of criticality in the statistics of neural activity recorded from populations of retinal ganglion cells, and hypothesized that the retina might be optimised to be operating at this critical point. What mechanisms can explain these observations? Do they require the neural system to be fine-tuned to be poised at the critical point, or do they robustly emerge in generic circuits? We here show that these effects arise in a simple, canonical models of retinal population activity. They robustly appear across a range of parameters, and can be understood analytically in a simple model. These observations pose the question of whether signatures of criticality are indicative of an optimised coding strategy, or whether alternative theories are more promising candidates for understanding sensory coding.