Deep learning models for atypical serotonergic cells recognition.

Journal: Journal of neuroscience methods
Published Date:

Abstract

BACKGROUND: The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied.

Authors

  • Daniele Corradetti
    Grupo de Fisica Matematica, Instituto Superior Tecnico, Av. Rovisco Pais, Lisboa, 1049-001, Portugal; Departamento de Matematica, Universidade do Algarve, Campus de Gambelas, Faro, 8005-139, Faro, Portugal. Electronic address: a55944@ualg.pt.
  • Alessandro Bernardi
    Am Brunnenbächli 22, Zollikerberg, Zollikon, 8125, Schweiz, Switzerland. Electronic address: balessandrob@gmail.com.
  • Renato Corradetti
    Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Viale G. Pieraccini 6, Firenze, 50139, Toscana, Italy. Electronic address: renato.corradetti@unifi.it.