Mathematical and statistical models have played important roles in neuroscience, especially
by describing the electrical activity of neurons recorded individually, or collectively across …

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw
nervous stimuli and respond identically to biological neurons. However, designing such …

Single-neuron models are useful not only for studying the emergent properties of neural
circuits in large-scale simulations, but also for extracting and summarizing in a principled …

Recent results demonstrate techniques for fully quantitative, statistical inference of the
dynamics of individual neurons under the Hodgkin–Huxley framework of voltage-gated …

Cardiac rhythm management devices provide therapies for both arrhythmias and
resynchronisation but not heart failure, which affects millions of patients worldwide. This …

We report on the construction of neuron models by assimilating electrophysiological data
with large-scale constrained nonlinear optimization. The method implements interior point …

When handling real-world data modeled by a complex network dynamical system, the
number of the parameters is often much more than the size of the data. Therefore, in many …

The construction of biologically relevant neuronal models as well as model-based analysis
of experimental data often requires the simultaneous fitting of multiple model parameters, so …

The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological
dynamics of a single neuron. While applying a low-amplitude constant current to the system …

Abstract The use of Hodgkin–Huxley (HH) equations abounds in the literature, but the
identifiability of the HH model parameters has not been broadly considered. Identifiability …