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Neuronal Coding Strategies for Two-Alternative Forced Choice Tasks

  • Erich L. Schulzke
  • Christian W. Eurich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

We investigated the connection between electrophysiological properties of neural populations and their ability to discriminate between the presence of one and two stimuli in a two-alternative forced choice task. The model is based on maximum likelihood estimation in a stimulus space that allows for the presence of multiple stimuli. Repetitive presentation of virtual stimuli yields receiver–operator–characteristics (ROC) curves and psychometric functions from noisy neural responses. For the case of one-dimensional stimuli like the movement direction of a random dot cloud we tested two coding strategies for discriminative ability. It turns out that narrow tuning curves and a variability of tuning widths within the neural population yields a high percentage of correct responses in the simulated psychophysical discrimination task. These results are similar to findings about the localization of single stimuli by neural populations: The examined encoding strategies lead to both an improvement of single stimulus estimation and discrimination between one and two stimuli.

Keywords

Psychometric Function Tuning Curve Single Stimulus Neural Population Tuning Width 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Erich L. Schulzke
    • 1
  • Christian W. Eurich
    • 1
  1. 1.Institut für Theoretische PhysikUniversität BremenBremenGermany

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