Abstract
The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.
Highlights
Ever since the seminal study of Hodgkin and Huxley [1] on the biophysical basis of the squid giant axon action potential, conductance-based models (CBMs) have provided a critical connection between the microscopic level of membrane ion channels and the macroscopic level of signal flow in neuronal circuits
Neurons perform complicated non-linear transformations on their input before producing their output - a train of action potentials
This input-output transformation is shaped by the specific composition of ion channels, out of the many possible types, that are embedded in the neuron’s membrane
Summary
Ever since the seminal study of Hodgkin and Huxley [1] on the biophysical basis of the squid giant axon action potential, conductance-based models (CBMs) have provided a critical connection between the microscopic level of membrane ion channels and the macroscopic level of signal flow in neuronal circuits. Successful matching of model response to a given target experimental data set is not, in and of itself, sufficient to establish the validity of a model, as complex models with numerous parameters run the risk of systematic biases (or errors) in the estimation of their parameters, i.e. over-fitting. Such a bias may not be apparent in the accuracy of matching the response to stimuli used to construct the model, but may be revealed by further testing of the model’s generalization to different conditions. One can imagine many different such tests: predicting the response to pharmacological manipulation, examining the stability of the model to small perturbations of model parameter values, etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
