Comparing Surrogates to Evaluate Precisely Timed Higher-Order Spike Correlations

eNeuro, vol. 9, pp. ENEURO.0505–21.2022

Abstract

The generation of surrogate data, i.e., the modification of data to destroy a certain feature, can be considered as the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid altering the single spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly distributed, to destroy temporal properties on a fine timescale while keeping them on a coarser one. Here, we compare UD against five similar surrogate techniques in the context of the detection of significant spatiotemporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled nonstationary artificial data to assess the potential detection of false positive (FP) patterns in a more complex and realistic setting. We determine which statistical features of the spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of FP patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from the pre-/motor cortex of macaque monkeys during a reaching-and-grasping task.

Authors 4

1. Alessandra Stella Aachen Theoretical Systems Neurobiology

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany a.stella@fz-juelich.de

   Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany

   Jülich Research Centre

   RWTH Aachen University

2. Peter Bouss Aachen Theoretical Systems Neurobiology

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany

   Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany

   Jülich Research Centre

   RWTH Aachen University

3. Günther Palm

   Forschungszentrum Jülich · Universität Ulm · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neural Information Processing, Ulm University, 89069 Ulm, Germany

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany

   Jülich Research Centre

   Ulm University

4. Sonja Grün Aachen Theoretical Systems Neurobiology

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52428 Jülich, Germany

   Theoretical Systems Neurobiology, RWTH Aachen University, 52062 Aachen, Germany

   Jülich Research Centre

   RWTH Aachen University