The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron’s lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiment and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry

Glass transition under confinement-what can be learned from calorimetry

Calorimetry is an effective analytical tool to characterize the glass transition and phase transitions under confinement. Calorimetry offers a broad dynamic range regarding heating and cooling rates, including isothermal and temperature modulated operation. Today 12 orders of magnitude in scanning rate can be covered by combining different types of calorimeters. The broad dynamic range, comparable to dielectric spectroscopy, is especially of interest for the study of kinetically controlled processes like crystallization or glass transition. Accuracy of calorimetric measurements is not very high. Commonly it does not reach 0.1% and often accuracy is only a few percent. Nevertheless, calorimetry can reach high sensitivity and reproducibility. Both are of particular interest for the study of confined systems. Low addenda heat capacity chip calorimeters are capable to measure the step in heat capacity at the glass transition in nanometer thin films. The good reproducibility is used for the study of glass forming materials confined by nanometer sized structures, like porous glasses, semicrystalline structures, nanocomposites, phase separated block copolymers, etc. Calorimetry allows also for the frequency dependent measurement of complex heat capacity in a frequency range covering several orders of magnitude. Here I exclusively consider calorimetry and its application to glass transition in confined materials. In most cases calorimetry reveals only a weak dependence of the glass transition temperature on confinement as long as the confining dimensions are above 10 nm. Why these findings contradict many other studies applying other techniques to similar systems is still an unsolved problem of glass transition in confinement