Cell-specific Ion-Channel Kinetics Improve Neuronal Model Fitting Across Chaotic Dynamical Regimes

Abstract

Modeling neuronal activity is central to understanding brain dynamics, yet fitting biophysically realistic neuron models to electrophysiological data remains difficult because neuronal membranes are highly nonlinear and their responses depend strongly on the dynamical regime explored. A major practical barrier is that standard fitting strategies often rely on fixed (“one-size-fits-all”) parameter sets and error functions that inadequately reflect spike waveform structure, leading to fits that appear good under one protocol but fail to generalize under other protocols. Here we show that pseudo-noisy (chaotic) stimuli expose regime-dependent limits of fixed-parameter neuron models, and that robust performance requires cell-specific kinetic fitting coupled to feature-based evaluation. We designed a library of pseudo-noisy current stimuli that vary in amplitude distributions, temporal structure, and frequency content, and applied them during whole-cell patch clamp recordings from premotor cortex neurons in zebra finches. We then drove a biophysically realistic conductance-based model, with ionic currents guided by pharmacological identification, using the same stimuli. Using a nonlinear data-assimilation framework, we estimated unobserved state variables and unknown parameters from responses to one chaotic stimulus and tested generalization by predicting responses to other chaotic stimuli. We found that a model fitted under a single chaotic input often reproduces neuronal behavior only within the dynamical regime explored by that stimulus; when a different chaotic stimulus drives the neuron through new voltage ranges and gating-variable states, fixed-parameter fits frequently break down, revealing mismatches in underlying ion-channel dynamics. In contrast, treating each neuron individually and fitting its activation/inactivation kinetics yields markedly improved fits and more reliable cross stimulus predictions. To quantify these effects, we introduced a feature-based error function tailored to chaotic stimulation, capturing spike timing and waveform heuristics (e.g., threshold, amplitude, width/time-to-peak, after-hyperpolarization, and subthreshold structure). Together, our results provide a principled framework - chaotic stimulus design, cell-specific kinetic identification, and feature-based error trade-off analysis, for evaluating when neuron models accurately capture ion-channel dynamics and when apparent fits are regime-restricted.