2010 Special Issue: Optimization of population decoding with distance metrics

  • Authors:

  • Sonja B. Hofer;Thomas D. Mrsic-Flogel;Domonkos Horvath;Benedikt Grothe;Nicholas A. Lesica

  • Affiliations:

  • Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1 6JJ, UK;Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1 6JJ, UK;Department of Biology, Ludwig-Maximilians-University Munich, Martinsried 02152, Germany and Faculty of Information Technology, Péter Pázmány Catholic University, Budapest, 1083, Hun ...;Department of Biology, Ludwig-Maximilians-University Munich, Martinsried 02152, Germany;Department of Biology, Ludwig-Maximilians-University Munich, Martinsried 02152, Germany

  • Venue:
  • Neural Networks
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Recent advances in multi-electrode recording and imaging techniques have made it possible to observe the activity of large populations of neurons. However, to take full advantage of these techniques, new methods for the analysis of population responses must be developed. In this paper, we present an algorithm for optimizing population decoding with distance metrics. To demonstrate the utility of this algorithm under experimental conditions, we evaluate its performance in decoding both population spike trains and calcium signals with different correlation structures. Our results demonstrate that the optimized decoder outperforms other simple population decoders and suggest that optimization could serve as a tool for quantifying the potential contribution of individual cells to the population code.