Fluctuation scaling in neural spike trains

TL;DR

This paper explores fluctuation scaling in neural spike trains, linking inter-event intervals and counting statistics, and demonstrates how different input regimes influence scaling behavior in neuron models.

Contribution

It formulates fluctuation scaling for neural event series and shows how input conditions affect scaling exponents in neuron models.

Findings

Fluctuation scaling relates mean and variance in neural spike data.

Scaling exponents vary with input regimes and excitation-inhibition balance.

Results have implications for understanding neural coding mechanisms.

Abstract

Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have considered the first-passage time of an Ornstein-Uhlenbeck process and used a conductance-based neuron model with excitatory and inhibitory synaptic inputs to demonstrate the emergence of fluctuation scaling with various exponents, depending on the input regimes and the ratio between excitation and inhibition. Furthermore, we have discussed the possible implication of these results in the context of neural coding.