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 experiments 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 is 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.

Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties

Sean Lewis Hill, Henry Markram, Felix Schürmann, Idan Segev

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na(+)-spiking behavior as well as key dendritic active properties, including Ca(2+) spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca(2+) spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.

2011

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

Synaptic plasticity across different time scales and its functional implications

Claudia Clopath

Humans and animals learn by modifying the synaptic strength between neurons, a phenomenon known as synaptic plasticity. These changes can be induced by rather short stimuli (lasting, for instance, only a few seconds), yet, in order to be useful for long-term memory, they should remain stable for months or years. Experimentalists study synaptic plasticity by applying a vast variety of protocols. In the present thesis we focus on protocols that fall under two main categories: (i) Those that induce synaptic modifications that last for only a few hours ("early phase" of plasticity) (ii) Those that allow synapses to undergo a sequence of steps that transforms the rapid changes occurring during the "early phase" into a stable memory trace ("late phase" of plasticity). The goal of this thesis is to better understand synaptic plasticity across these different phases, early and late, by creating compact mathematic models to describe the plasticity mechanisms. Our approach allows for a synthetic view of the field as well as the exploration of functional consequences of learning. In this direction, we propose a model for the induction of synaptic plasticity that depends on the presynaptic spike time and nonlinearly on the postsynaptic voltage. The model is able to reproduce a broad range of experimental protocols such as voltage-clamp experiments and spike-timing experiments. Since the voltage is a key element in the model, we describe the neuronal activity by using a compact neuron model that faithfully reproduces the voltage time course of pyramidal neurons. In addition, this model of the induction of synaptic plasticity is combined with a trigger process for protein synthesis, and the final stabilization mechanism in order to describe the "late phase". In this combinatory form, it is able to explain experimental phenomena known as tagging experiments and to make testable predictions. A study of functional consequences of the induction model reveals selectivity in the inputs, independent component analysis computation and a tight relation between connectivity and coding. In parallel a top-down approach finding independent components is used to derive a rate-based learning rule which shows structural correlations with the induction model. This unified model across different time scales allowing the stabilization of synapses is crucial to understand learning and memory processes in animals and humans, and a necessary ingredient for any large-scale model of the brain.