P100 Dynamic Range Revisited: Novel Methods for Accurate Characterization of Complex Response Functions
Jenna Richardson1, Filipe V. Torres2, Mauro Copelli2 ,Leonardo L. Gollo*1,3
1Brain Networks and Modelling Laboratory and The Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia. 2Departamento de Física, Centro de Ciência Exatas e da Natureza, Universidade Federal de Pernambuco, Recife, Brazil. 3Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Palma de Mallorca, Spain.
*Email: leonardo@ifisc.uib-csic.es
Introduction
The neuronal response function maps external stimuli to neural activity, with dynamic range quantifying the input levels producing distinguishable responses. Traditional sigmoidal response functions exhibit minimal firing rate changes at low and high inputs, with marked shifts at intermediate levels, making the 10%-90% response range a reliable dynamic range measure [1]. However, complex response functions [2-11]—such as double-sigmoid or multi-sigmoidal profiles with plateaus—challenge conventional calculations, often overestimating dynamic range. To address this, we propose a classification of response function complexity and introduce alternative dynamic range definitions for accurate quantification. Methods We analyzed a set of previously published empirical and computational studies featuring both simple and complex response functions. Additionally, we examined a neuronal model of a mouse retinal ganglion cell with a detailed dendritic structure, capable of generating both simple-sigmoid and complex response profiles. The model incorporated two dynamical elements that modulate energy consumption, either reducing or increasing neuronal activity, leading to the emergence of double-sigmoid response functions. To refine dynamic range estimation, we developed four alternative definitions that selectively consider only discernible response variations while excluding plateaus. These methods were evaluated by comparing their performance with the conventional definition across a range of response functions. Results Our findings confirm that the conventional 10%-90% dynamic range definition is effective for simple response functions but often inflates the estimated range for complex profiles due to the inclusion of plateau regions. In contrast, our proposed alternative definitions successfully differentiate meaningful response regions from indistinguishable input levels. Each method produced results that aligned with conventional calculations for simple response functions while offering a more precise generalization for complex cases. Moreover, the neuronal model demonstrated that specific modifications in dendritic dynamics can induce complex response profiles, reinforcing the necessity of improved measurement approaches. Discussion Our study reveals the limitations of traditional dynamic range definitions in capturing neuronal response diversity. The proposed classification and alternative calculations reduce arbitrary assumptions, enhancing accuracy across neuronal systems. These methods are generalizable beyond neuroscience, applicable to fields with complex, nonlinear dynamics. Freely available computational tools promote adoption and refinement. By improving dynamic range estimation,this work enhances our understanding of complex response functions.
Acknowledgements This work was supported by the Australian Research Council (ARC) Future Fellowship (FT200100942), the Ramón y Cajal Fellowship (RYC2022-035106-I), and the María de Maeztu Program for units of Excellence in R&D, grant CEX2021-001164-M/10.13039/501100011033. References 1. https://doi.org/10.1371/journal.pcbi.1000402 2. https://doi.org/10.1016/S0896-6273(02)01046-2 3. https://doi.org/10.1103/PhysRevE.85.011911 4. https://doi.org/10.1016/S0378-5955(02)00293-9 5. https://doi.org/10.1021/ja209850j 6. https://doi.org/10.1006/bbrc.1999.1375 7. https://doi.org/10.1103/PhysRevE.85.040902 8. https://doi.org/10.1038/srep03222 9. https://doi.org/10.1038/s41598-023-34454-8 10. https://doi.org/10.1073/pnas.0904784106 11. https://doi.org/10.1007/978-1-4419-0194-1_10
