Analysing limited electrophysiological data by artificial sample generation

LUCAS MOLA; CAMILO JUAN MININNI; B. SILVANO ZANUTTO; SERGIO E. LEW

Congreso; Congreso de la Sociedad Argentina de Neurociencias; 2014

During electrophysiological studies of animal behavior, subjects are required to achieve a certain (high) performance, in order to consider that they have learned a task. Given the finite number of trials an animal executes during a session before it gets satiated or exhausted, incorrect trials are a minority and statistically hard to analyze. When the purpose of a research is to understand the neural mechanisms that lead to an incorrect response, robust procedures to extract information from limited trials are required.In this work we propose a new method for the generation of samples which retain the statisticsexhibited by real extracellular multielectrode recordings. The method compensates the bias produced by a reduced number of samples, and is tested for several statistical measures.