STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity

Background:

In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.

New method:

Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.

Results:

The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.

Comparison of existing method:

The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels.

Conclusion:

ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures — ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore — capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.