Capacitance measurement of dendritic exocytosis in an electrically coupled inhibitory retinal interneuron: an experimental and computational study

Exocytotic release of neurotransmitter can be quantified by electrophysiological recording from postsynaptic neurons. Alternatively, fusion of synaptic vesicles with the cell membrane can be measured as increased capacitance by recording directly from a presynaptic neuron. The “Sine + DC” technique is based on recording from an unbranched cell, represented by an electrically equivalent RC-circuit. It is challenging to extend such measurements to branching neurons where exocytosis occurs at a distance from a somatic recording electrode. The AII amacrine is an important inhibitory interneuron of the mammalian retina and there is evidence that exocytosis at presynaptic lobular dendrites increases the capacitance. Here, we combined electrophysiological recording and computer simulations with realistic compartmental models to explore capacitance measurements of rat AII amacrine cells. First, we verified the ability of the “Sine + DC” technique to detect depolarizationevoked exocytosis in physiological recordings. Next, we used compartmental modeling to demonstrate that capacitance measurements can detect increased membrane surface area at lobular dendrites. However, the accuracy declines for lobular dendrites located further from the soma due to frequency-dependent signal attenuation. For sine wave frequencies ≥1 kHz, the magnitude of the total releasable pool of synaptic vesicles will be significantly underestimated. Reducing the sine wave frequency increases overall accuracy, but when the frequency is sufficiently low that exocytosis can be detected with high accuracy from all lobular dendrites (~100 Hz), strong electrical coupling between AII amacrines compromises the measurements. These results need to be taken into account in studies with capacitance measurements from these and other electrically coupled neurons.publishedVersio

Electrotonic signal processing in AII amacrine cells: compartmental models and passive membrane properties for a gap junction-coupled retinal neuron

Under embargo until: 14.06.2019Amacrine cells are critical for processing of visual signals, but little is known about their electrotonic structure and passive membrane properties. AII amacrine cells are multifunctional interneurons in the mammalian retina and essential for both rod- and cone-mediated vision. Their dendrites are the site of both input and output chemical synapses and gap junctions that form electrically coupled networks. This electrical coupling is a challenge for developing realistic computer models of single neurons. Here, we combined multiphoton microscopy and electrophysiological recording from dye-filled AII amacrine cells in rat retinal slices to develop morphologically accurate compartmental models. Passive cable properties were estimated by directly fitting the current responses of the models evoked by voltage pulses to the physiologically recorded responses, obtained after blocking electrical coupling. The average best-fit parameters (obtained at − 60 mV and ~ 25 °C) were 0.91 µF cm−2 for specific membrane capacitance, 198 Ω cm for cytoplasmic resistivity, and 30 kΩ cm2 for specific membrane resistance. We examined the passive signal transmission between the cell body and the dendrites by the electrotonic transform and quantified the frequency-dependent voltage attenuation in response to sinusoidal current stimuli. There was significant frequency-dependent attenuation, most pronounced for signals generated at the arboreal dendrites and propagating towards the soma and lobular dendrites. In addition, we explored the consequences of the electrotonic structure for interpreting currents in somatic, whole-cell voltage-clamp recordings. The results indicate that AII amacrines cannot be characterized as electrotonically compact and suggest that their morphology and passive properties can contribute significantly to signal integration and processing.acceptedVersio

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies

Neural Dynamics during Anoxia and the “Wave of Death”

Recent experiments in rats have shown the occurrence of a high amplitude slow brain wave in the EEG approximately 1 minute after decapitation, with a duration of 5–15 s (van Rijn et al, PLoS One 6, e16514, 2011) that was presumed to signify the death of brain neurons. We present a computational model of a single neuron and its intra- and extracellular ion concentrations, which shows the physiological mechanism for this observation. The wave is caused by membrane potential oscillations, that occur after the cessation of activity of the sodium-potassium pumps has lead to an excess of extracellular potassium. These oscillations can be described by the Hodgkin-Huxley equations for the sodium and potassium channels, and result in a sudden change in mean membrane voltage. In combination with a high-pass filter, this sudden depolarization leads to a wave in the EEG. We discuss that this process is not necessarily irreversible

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Neuronal activity and ion homeostasis in the hypoxic brain

The interruption of blood flow to the brain as occurs in cardiac arrest and stroke results within minutes in irreversible damage. The development of neuroprotective treatments that prevent cell damage after stroke has so far largely been unsuccessful, while we still have an incomplete understanding of the dynamics of the processes involved. This thesis has focused on the dynamics of ion concentrations and neuronal activity during and after hypoxia. In chapter 3, it was shown that the extracellular potassium concentration has a crucial role in anoxic depolarization. If potassium fluxes are insufficiently compensated by the ATP-dependent Na/K pumps, the extracellular concentration may reach a critical concentration, resulting in an additional, significant increase in potassium efflux. This efflux induces massive depolarization of the neurons, reflected as a transient wave of activity on the scalp EEG. This explains the misnamed ``Wave of Death'' that is observed in rats after decapitation. The released potassium does not only excite the neurons that released it, but also diffuses to neighboring cells, causing a chain reaction or ``reaction-diffusion'' process. Chapter 4 has presented expressions for the initiation, propagation and wave shape of idealized spreading depolarization (SD). The predicted shape of the onset of the wave was validated with potassium measurements in vivo in rat from literature. The model used to calculate the behavior of depolarizing single neurons in the previous two chapters, the Hodgkin-Huxley (HH) model with dynamic ion concentrations, is experimentally validated in Chapter 5. Several time courses of depolarizing pyramidal cells were obtained in in-vitro experiments after blocking the Na/K-pump with ouabain. Five different types of membrane voltage dynamics were observed. These correspond to different trajectories of the sodium and potassium Nernst potentials in a bifurcation diagram of the HH model. In chapter 6, an initial effort was made in modeling the behavior of large populations of neurons (neural mass models), rather than single ones, with pathological transmembrane ion gradients. The firing rate curve was used as link between the single cell and neural mass model. This model excellently reproduces the dynamics observed in a simulated network of HH-neurons