Kostal2025OBIDLIF

Title

Optimal Boundary for Input Detection with LIF Neuronal Models
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Authors

Lubomir Kostal, Laura Sacerdote, Cristina Zucca

Affiliations

Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic; Department of Mathematics, University of Torino, Via C. Alberto 10, 10138 Torino, Italy.

Abstract

We investigate information transmission in neuronal models based on Brownian motion and the Ornstein–Uhlenbeck process, in which neuronal spiking times are modeled as first passage times through oscillating boundaries. Using mutual information and mutual information per unit time as metrics, we analyze how boundary oscillation parameters and input variability influence coding efficiency. Our analysis reveals complex dependencies on input variability and diffusion strength, including non-monotonic effects of input variance and unexpected increases in information with diffusion strength in the Ornstein–Uhlenbeck case. We also identify the existence of optimal oscillation frequencies, whose values depend on the specific information measure used.

KeyPhrases

Ornstein-Uhlenbeck process, first passage time, mutual information, input-specific information.

Dates

Created 2025-05-01, presented 2025-06-02, updated 2025-09-01, published 2025-09-02.

Citation

Brainiacs Journal 2025 Volume 6 Issue 1 Edoc K46051E45
DOI: 10.48085/K46051E45
NPDS: LINKS/Brainiacs/Kostal2025OBIDLIF