Fluctuation scaling in neural spike trains

Area of Science:

• Physics
• Neuroscience
• Complex Systems

Background:

• Fluctuation scaling, a universal phenomenon, describes power-law relationships in time series data.
• In event sequences, this scaling relates mean and variance of inter-event intervals or counting statistics.
• Understanding scaling in neural activity is crucial for deciphering neural coding.

Purpose of the Study:

• To formulate fluctuation scaling for event series, linking inter-event interval and counting statistics.
• To investigate the emergence of fluctuation scaling in a biophysical neuron model.
• To explore the influence of synaptic input regimes on scaling exponents.

Main Methods:

• Formulation of fluctuation scaling for event series.
• Analysis of the first-passage time of an Ornstein-Uhlenbeck process.
• Simulation of a conductance-based neuron model with varying excitatory and inhibitory synaptic inputs.

Main Results:

• Demonstrated a unified framework for fluctuation scaling in inter-event intervals and counting statistics.
• Showcased the emergence of diverse fluctuation scaling exponents in a neuron model.
• Identified input regimes and excitation-inhibition balance as key determinants of scaling exponents.

Conclusions:

• Fluctuation scaling in neural activity is directly linked to the dynamics of synaptic inputs.
• The observed scaling laws provide a potential mechanism for efficient neural coding.
• This work bridges the gap between statistical physics and computational neuroscience.