Robust stimulus detection with imprecise spiking phase

  • Precise timing of spikes between different neurons has been found to convey reliable information beyond the spike count. In contrast, the role of small phase delays with high temporal variability, as reported for example in oscillatory activity in the visual cortex, remains largely unclear. This issue becomes particularly important considering the high speed of neuronal information processing, which is assumed to be based on only a few milliseconds, or oscillation cycles within each processing step. We investigate the role of small and imprecise phase delays with a stochastic spiking model that is strongly motivated by experimental observations. Within individual oscillation cycles the model contains only two signal parameters describing directly the rate and the phase. We specifically investigate two quantities, the probability of correct stimulus detection and the probability of correct change point detection, as a function of these signal parameters and within short periods of time such as individual oscillation cycles. Optimal combinations of the signal parameters are derived that maximize these probabilities and enable comparison of pure rate, pure phase and combined codes. In particular, the gain in detection probability when adding imprecise phases to pure rate coding increases with the number of stimuli. More interestingly, imprecise phase delays can considerably improve the process of detecting changes in the stimulus, while also decreasing the probability of false alarms and thus, increasing robustness and speed of change point detection. The results are applied to parameters extracted from empirical spike train recordings of neurons in the visual cortex in response to a number of visual stimuli. The results suggest that near-optimal combinations of rate and phase parameters can be implemented in the brain, and that phase parameters could particularly increase the quality of change point detection in cases of highly similar stimuli.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Benjamin Straub
Place of publication:Frankfurt am Main
Referee:Gaby SchneiderGND, Anton WakolbingerGND
Document Type:Doctoral Thesis
Date of Publication (online):2018/12/11
Year of first Publication:2018
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2018/12/10
Release Date:2018/12/14
Tag:online bayesian change point detection; optimal coding; phase coding; spike timing; stochastic model
Page Number:viii, 204
Institutes:Informatik und Mathematik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Licence (German):License LogoDeutsches Urheberrecht