Deciphering neural communication: can a new spike distance measure improve discrimination between spike trains? This study introduces a novel metric for quantifying the distance between spike trains, offering insights into neural coding and neuronal noise. The research presents a distance measure with a time constant parameter that interpolates between a coincidence detector and a rate difference counter. By varying this parameter, the measure captures different aspects of spike train similarity. The authors also studied the distance's sensitivity to noise using an integrate-and-fire model. The findings reveal that for an intermediate range of time constants, the distance exhibits a linear dependence on noise, potentially enabling the determination of a neuron's intrinsic noise level. This innovative approach contributes to understanding neural coding mechanisms and characterizing neuronal variability. The proposed spike distance measure may find applications in analyzing neural data and improving neural signal processing techniques.
Published in Neural Computation, this research aligns with the journal's focus on theoretical and computational neuroscience. By introducing a new measure for quantifying the distance between spike trains, the paper contributes to understanding neural coding and information processing in the nervous system. This aligns with the journal’s emphasis on advancing computational methods in neuroscience.