A Multimodal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High-Density Microelectrode Arrays.

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

A universal workflow for creation, validation, and generalization of detailed neuronal models.

Detailed single-neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools focus on a limited set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.

Computational Model for Cross-Depolarization in DRG Neurons via Satellite Glial Cells using [K]o: Role of Kir4.1 Channels and Extracellular Leakage.

Satellite glial cells (SGCs) are glial cells found in the peripheral nervous system where they tightly envelop the somata of the primary sensory neurons such as dorsal root ganglion (DRG) neurons and nodose ganglion (NG) neurons. The somata of these neurons are generally compactly packed in their respective ganglia (DRG and NG). SGCs covering a neuron behave as an insulator of electrical activity from neighbouring neurons within the ganglion. Several studies have however shown that the somata show "cross-depolarization" (CD). Origin of CDs has been hypothesized to be chemical in nature: either from neurotransmitter release from both SGCs and somata or from elevation of extracellular potassium concentration ([K]o) in the vicinity of somata. Here, we investigate the role of Kir4.1 channels on SGC and diffusion/clearance factor (ß) of [K]o from the space between SGC and DRG neuron somata to the bulk extracellular space in ganglion. We show using two "Soma-SGC Units" interacting via gap junction that a combination of Kir4.1 and ß could be responsible for CD between DRG neuron somata in pathological conditions.

A biophysically detailed computational model of urinary bladder small DRG neuron soma.

Bladder small DRG neurons, which are putative nociceptors pivotal to urinary bladder function, express more than a dozen different ionic membrane mechanisms: ion channels, pumps and exchangers. Small-conductance Ca2+-activated K+ (SKCa) channels which were earlier thought to be gated solely by intracellular Ca2+ concentration ([Ca]i) have recently been shown to exhibit inward rectification with respect to membrane potential. The effect of SKCa inward rectification on the excitability of these neurons is unknown. Furthermore, studies on the role of KCa channels in repetitive firing and their contributions to different types of afterhyperpolarization (AHP) in these neurons are lacking. In order to study these phenomena, we first constructed and validated a biophysically detailed single compartment model of bladder small DRG neuron soma constrained by physiological data. The model includes twenty-two major known membrane mechanisms along with intracellular Ca2+ dynamics comprising Ca2+ diffusion, cytoplasmic buffering, and endoplasmic reticulum (ER) and mitochondrial mechanisms. Using modelling studies, we show that inward rectification of SKCa is an important parameter regulating neuronal repetitive firing and that its absence reduces action potential (AP) firing frequency. We also show that SKCa is more potent in reducing AP spiking than the large-conductance KCa channel (BKCa) in these neurons. Moreover, BKCa was found to contribute to the fast AHP (fAHP) and SKCa to the medium-duration (mAHP) and slow AHP (sAHP). We also report that the slow inactivating A-type K+ channel (slow KA) current in these neurons is composed of 2 components: an initial fast inactivating (time constant ∼ 25-100 ms) and a slow inactivating (time constant ∼ 200-800 ms) current. We discuss the implications of our findings, and how our detailed model can help further our understanding of the role of C-fibre afferents in the physiology of urinary bladder as well as in certain disorders.

Computational studies on bladder small dorsal root ganglion neurons: Modelling BK channels.

The urinary bladder afferent neurons called the dorsal root ganglion (DRG) neurons carry information on diverse modalities such as stretch, pressure and nociception to the spinal cord. This information is carried in the form of electrical activity called action potentials (AP). The bladder small diameter DRG neurons that are considered to be putative nociceptors express several ion channels and active mechanisms which are responsible for generating this electrical activity. One of the channels that has been suggested to play a role in cell excitability is the large conductance calcium activated potassium channel (BK) channel. Its activation is governed by cell membrane potential and intracellular calcium concentration. Here, we present a computational model of the BK channel along with other ion channels and mechanisms present in the bladder small DRG neuron cell body. The BK channel simulations show properties that are similar to those shown by Isolectin B4 (IB4) negative cutaneous small DRG neurons. The bladder small DRG neurons have also been found to show some of these properties. Thus, we hypothesize that the bladder small DRG neurons are IB4 negative. This hypothesis is supported by experimental studies which suggest that about 80% of bladder small DRG neurons are IB4 negative. The model of bladder small DRG neuron also faithfully reproduced some of the electrical properties that have been reported experimentally. This model can thus be used to predict abnormal behaviour of the DRG neuron during pathological conditions.