Frequency preference and reliability of signal integration

Die Eigenschaften einzelner Nervenzellen sind von grundlegender Bedeutung für die Verarbeitung von Informationen im Nervensystem. Neuronen antworten auf Eingangsreize durch Veränderung der elektrischen Spannung über die Zellmembran. Die Spannungsantwort wird dabei durch die Dynamik der Ionenkanäle in der Zellmembran bestimmt. In dieser Arbeit untersuche ich anhand von leitfähigkeits-basierten Modellneuronen den Einfluss von Ionenkanälen auf zwei Aspekte der Signalverarbeitung: die Frequenz-Selektivität sowie die Zuverlässigkeit und zeitliche Präzision von Aktionspotentialen. Zunächst werden die zell-intrinsischen Mechanismen identifiziert, welche the Frequenz-Selektivität und die Zuverlässigkeit bestimmen. Weiterhin wird untersucht, wie Ionenkanäle diese Mechanismen modulieren können, um die Integration von Signalen zu optimieren. Im ersten Teil der Arbeit wird demonstriert, dass der Mechanismus der unterschwelligen Resonanz, so wie er bisher für periodische Signale beobachtet wurde, auch auf nicht-periodische Signale anwendbar ist und sich ebenfalls in den Feuerraten niederschlägt. Im zweiten Teil wird gezeigt, dass zeitliche Präzision und Zuverlässigkeit von Aktionspotentialen mit der Stimulusfrequenz variieren und dass, in Abhängigkeit davon, ob das Stimulusmittel über- oder unterhalb der Feuerschwelle liegt, zwei Stimulusregime unterschieden werden müssen. In beiden Regimen existiert eine bevorzugte Stimulusfrequenz, welche durch die Gesamtleitfähigkeit und die Dynamik spezifischer Ionenkanäle moduliert werden kann. Im dritten Teil wird belegt, dass Ionenkanäle die Zuverlässigkeit auch direkt über eine Veränderung der Sensitivität einer Zelle gegenüber neuronalem Rauschen bestimmen können. Die Ergebnisse der Arbeit lassen auf eine wichtige Rolle der dynamischen Regulierung der Ionenkanäle für die Frequenz-Selektivität und die zeitliche Präzision und Zuverlässigkeit der Spannungsantworten schließen.The properties of individual neurons are of fundamental importance for the processing of information in the nervous system. The generation of voltage responses to input signals, in particular, depends on the properties of ion channels in the cell membrane. Within this thesis, I employ conductance-based model neurons to investigate the effect of ionic conductances and their dynamics on two aspects of signal processing: frequency-selectivity and temporal precision and reliability of spikes. First, the cell-intrinsic mechanisms that determine frequency selectivity and spike timing reliability are identified on the basis of conductance-based model neurons. Second, it is analyzed how ionic conductances can serve to modulate these mechanisms in order to optimize signal integration. In the first part, the frequency selectivity of subthreshold response amplitudes previously observed for periodic stimuli is proven to extend to nonperiodic stimuli and to translate into firing rates. In the second part, it is demonstrated that spike timing reliability is frequency-selective and that two different stimulus regimes have to be distinguished, depending on whether the stimulus mean is below or above threshold. In both cases, resonance effects determine the most reliable stimulus frequency. It is shown that this frequency preference can be modulated by the peak conductance and dynamics of specific ion channels. In the third part, evidence is provided that ionic conductances determine spike timing reliability beyond changes in the preferred frequency. It is demonstrated that ionic conductances also exert a direct influence on the sensitivity of the timing of spikes to neuronal noise. The findings suggest an important role for dynamic neuromodulation of ion channels with regard to frequency selectivity and spike timing reliability

Intrinsic electrophysiological properties of entorhinal cortex stellate cells and their contribution to grid cell firing fields

The medial entorhinal cortex (MEC) is an increasingly important focus for investigation of mechanisms for spatial representation. Grid cells found in layer II of the MEC are likely to be stellate cells, which form a major projection to the dentate gyrus. Entorhinal stellate cells are distinguished by distinct intrinsic electrophysiological properties, but how these properties contribute to representation of space is not yet clear. Here, we review the ionic conductances, synaptic, and excitable properties of stellate cells, and examine their implications for models of grid firing fields. We discuss why existing data are inconsistent with models of grid fields that require stellate cells to generate periodic oscillations. An alternative possibility is that the intrinsic electrophysiological properties of stellate cells are tuned specifically to control integration of synaptic input. We highlight recent evidence that the dorsal-ventral organization of synaptic integration by stellate cells, through differences in currents mediated by HCN and leak potassium channels, influences the corresponding organization of grid fields. Because accurate cellular data will be important for distinguishing mechanisms for generation of grid fields, we introduce new data comparing properties measured with whole-cell and perforated patch-clamp recordings. We find that clustered patterns of action potential firing and the action potential after-hyperpolarization (AHP) are particularly sensitive to recording condition. Nevertheless, with both methods, these properties, resting membrane properties and resonance follow a dorsal-ventral organization. Further investigation of the molecular basis for synaptic integration by stellate cells will be important for understanding mechanisms for generation of grid fields

The role of Cav3.2 Ca2+ channels in influencing the activity of the layer II stellate cells of the Medial Entorhinal Cortex

Layer II (L II) Medial Entorhinal Cortex (MEC) stellate cell (SC) intrinsic membrane properties vary along the MEC dorsal-ventral axis. This has been attributed partly to altered HCN and K+ conductances (Garden et al. 2008; Giocomo and Hasselmo 2008). The subthreshold active T-type CaV3.2 Ca2+ channels, though, are also expressed in the MEC (Huang et al. 2011). CaV3.2 channels are known to influence neuronal excitability but their effects on dorsal and ventral LII MEC SC properties remain unknown. To investigate this, I obtained acute brain slices from CaV3.2 wild type (CaV3.2+/+) and null (CaV3.2-/-) 5-8 week old mice and made electrophysiological recordings from dorsal and ventral L II MEC SC. CaV3.2-/- ventral neurons displayed significantly reduced input resistance but little difference in resting membrane potential (RMP) compared with CaV3.2+/+ ventral neurons. Consequently, depolarizing steps resulted in fewer action potentials in CaV3.2-/- ventral SC than in wild type neurons. In contrast, dorsal CaV3.2-/- and CaV3.2+/+ SC properties were similar. Furthermore, CaV3.2+/+ ventral cells had a significantly higher α excitatory post synaptic potentials (αEPSP) summation ratio (at 50 Hz) in comparison to CaV3.2-/- ventral neurons. The Cav3 inhibitors, NiCl2 and TTA-P2, also significantly reduced input resistance and action potential firing in CaV3.2+/+ ventral neurons, whilst having little effect on CaV3.2+/+ dorsal or CaV3.2-/- neurons. Furthermore, voltage-clamp experiments revealed a significantly greater T-type Cav3.2 Ca2+ current in ventral than dorsal neurons. Our results suggest that Cav3.2 channels selectively affect L II MEC ventral SC properties, thereby contributing to the intrinsic membrane gradient across the MEC dorsal-ventral axis

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

Cellular properties of the medial entorhinal cortex as possible mechanisms of spatial processing

Cells of the rodent medial entorhinal cortex (EC) possess cellular properties hypothesized to underlie the spatially periodic firing behaviors of 'grid cells' (GC) observed in vivo. Computational models have simulated experimental GC data, but a consensus as to what mechanism(s) generate GC properties has not been reached. Using whole cell patch-clamp and computational modeling techniques this thesis investigates resonance, rebound spiking and persistent spiking properties of medial EC cells to test potential mechanisms generating GC firing. The first experiment tested the voltage dependence of resonance frequency in layer II medial EC stellate cells. Some GC models use interference between velocity-controlled oscillators to generate GCs. These interference mechanisms work best with a linear relationship between voltage and resonance frequency. Experimental results showed resonance frequency decreased linearly with membrane potential depolarization, suggesting resonance properties could support the generation of GCs. Resonance appeared in medial EC but not lateral EC consistent with location of GCs. The second experiment tested predictions of a recent network model that generates GCs using medial EC stellate cell resonance and rebound spiking properties. Sinusoidal oscillations superimposed with hyperpolarizing currents were delivered to layer II stellate cells. Results showed that relative to the sinusoid, a limited phase range of hyperpolarizing inputs elicited rebound spikes, and the phase range of rebound spikes was even narrower. Tuning model parameters of the stellate cell population to match experimental rebound spiking properties resulted in GC spatial periodicity, suggesting resonance and rebound spiking are viable mechanisms for GC generation. The third experiment tested whether short duration current inputs can induce persistent firing and afterdepolarization in layer V pyramidal cells. During muscarinic acetylcholine receptor activation 1-2 second long current injections have been shown to induce persistent firing in EC principal cells. Persistent firing may underlie working memory performance and has been used to model GCs. However, input stimuli during working memory and navigation may be much shorter than 1-2 seconds. Data showed that input durations of 10, 50 and 100 ms could elicit persistent firing, and revealed time courses and amplitude of afterdepolarization that could contribute to GC firing or maintenance of working memory

Specific Entrainment of Mitral Cells during Gamma Oscillation in the Rat Olfactory Bulb

Local field potential (LFP) oscillations are often accompanied by synchronization of activity within a widespread cerebral area. Thus, the LFP and neuronal coherence appear to be the result of a common mechanism that underlies neuronal assembly formation. We used the olfactory bulb as a model to investigate: (1) the extent to which unitary dynamics and LFP oscillations can be correlated and (2) the precision with which a model of the hypothesized underlying mechanisms can accurately explain the experimental data. For this purpose, we analyzed simultaneous recordings of mitral cell (MC) activity and LFPs in anesthetized and freely breathing rats in response to odorant stimulation. Spike trains were found to be phase-locked to the gamma oscillation at specific firing rates and to form odor-specific temporal patterns. The use of a conductance-based MC model driven by an approximately balanced excitatory-inhibitory input conductance and a relatively small inhibitory conductance that oscillated at the gamma frequency allowed us to provide one explanation of the experimental data via a mode-locking mechanism. This work sheds light on the way network and intrinsic MC properties participate in the locking of MCs to the gamma oscillation in a realistic physiological context and may result in a particular time-locked assembly. Finally, we discuss how a self-synchronization process with such entrainment properties can explain, under experimental conditions: (1) why the gamma bursts emerge transiently with a maximal amplitude position relative to the stimulus time course; (2) why the oscillations are prominent at a specific gamma frequency; and (3) why the oscillation amplitude depends on specific stimulus properties. We also discuss information processing and functional consequences derived from this mechanism

Theta-frequency resonance at the cerebellum input stage improves spike timing on the millisecond time-scale

The neuronal circuits of the brain are thought to use resonance and oscillations to improve communication over specific frequency bands (Llinas, 1988; Buzsaki, 2006). However, the properties and mechanism of these phenomena in brain circuits remain largely unknown. Here we show that, at the cerebellum input stage, the granular layer (GRL) generates its maximum response at 5\u20137 Hz both in vivo following tactile sensory stimulation of the whisker pad and in acute slices following mossy fiber bundle stimulation. The spatial analysis of GRL activity performed using voltage-sensitive dye (VSD) imaging revealed 5\u20137 Hz resonance covering large GRL areas. In single granule cells, resonance appeared as a reorganization of output spike bursts on the millisecond time-scale, such that the first spike occurred earlier and with higher temporal precision and the probability of spike generation increased. Resonance was independent from circuit inhibition, as it persisted with little variation in the presence of the GABAA receptor blocker, gabazine. However, circuit inhibition reduced the resonance area more markedly at 7 Hz. Simulations with detailed computational models suggested that resonance depended on intrinsic granule cells ionic mechanisms: specifically, Kslow (M-like) and KA currents acted as resonators and the persistent Na current and NMDA current acted as amplifiers. This form of resonance may play an important role for enhancing coherent spike emission from the GRL when theta-frequency bursts are transmitted by the cerebral cortex and peripheral sensory structures during sensory-motor processing, cognition, and learning

Studying the cortical state with transcranial magnetic stimulation

Cortical excitability and connectivity describe the state of the cerebral cortex. They reflect the ability of neurons to respond to input and the way information flows in the neuronal networks. These properties can be assessed with transcranial magnetic stimulation (TMS), which enables direct and noninvasive modulation of cortical activity. Electrophysiological or hemodynamic recordings of TMS-evoked activity or behavioral measures of the stimulation effect characterize the state of the cortex during and as a result of the stimulation. In the research reported in this Thesis, the ability of TMS to inform us about the cortical state is studied from different points of view. First, we examine the relationships between different measures of cortical excitability to better understand the physiology behind them; we show how cortical background activity is related to motor cortical excitability and how the evoked responses reflect the excitability. Second, this study addresses the questions whether the TMS-evoked responses include stimulation-related artifacts, how these artifacts are generated, and how they can be avoided or removed. Specifically, we present a method to remove the artifacts from TMS-evoked electroencephalographic (EEG) signals arising as a result of cranial muscle stimulation. The use of TMS-EEG has been limited to relatively medial sites because of these artifacts, but the new method enables studying the cortical state even when stimulating areas near the cranial muscles, especially lateral sites. Finally, this work provides new information about brain function. The mechanisms how the brain processes visually guided timed motor actions are elucidated. Moreover, we show that cortical excitability as measured with TMS-evoked EEG increases during the course of wakefulness and decreases during sleep, which contributes to our understanding of what happens in the brain during wakefulness that makes us feel tired and why the brain needs sleep. The study also shows the sensitivity of the TMS-EEG measurement to changes in the state of the cortex. Accordingly, we demonstrate the power of TMS in studying the cortical state

Functional dissection of a cortical microcircuit for spatial computation

In mammals, spatial learning and memory depend on neural processing carried out in the hippocampal formation. Interestingly, extracellular recordings from behaving animals have shown that cells in this region exhibit spatially modulated activity patterns, thus providing insights into the neural activity underlying spatial behaviour. One area within the hippocampal formation, layer II of the medial entorhinal cortex, houses cells that encode a grid-like map of space using a firing rate code. At the same time, oscillatory signals at distinct theta (4–12 Hz) and gamma (30–120 Hz) frequencies are also present in layer II, providing a substrate for a timing code. To understand how layer II of the medial entorhinal cortex produces these outputs I sought to characterise the electrical properties and functional computational architecture of its microcircuitry. The functionality of any neural circuit depends on the electrical properties of its constituent cells. Because the grid cells in layer II are likely to be stellate cells, I used the perforated patch-clamp technique to accurately assess the intrinsic excitable properties of this cell type. Compared to whole-cell recordings, these recordings indicate that some intrinsic properties of stellate cells, such as spike clustering, which is revealed to be robust, are more likely to play a functional role in circuit computation. Conversely, other intrinsic properties, such as spontaneous membrane potential fluctuations, which are confirmed to be insufficiently stable to support reliable interference patterns, are revealed to be less likely than other, more robust electrical properties to play a direct role in circuit function. The characteristic connectivity profiles of different cell types are also critical for circuit function. To investigate cell type-specific connectivity in layer II I used optogenetic stimulation in combination with in vitro electrophysiology to record synaptic activity in different cell types while selectively activating distinct subpopulations of cells with light. Using this method I found that connections between stellate cells are absent or very rare and that communication between stellate cells is instead mediated by strong feedback inhibition from fast-spiking interneurons. Dissecting oscillatory activity in neural circuits may be important for establishing functionally relevant circuit architecture and dynamics but is difficult in vivo. I accomplished this in vitro by recapitulating the interacting theta and gamma rhythms that are observed in vivo with an optogenetic method. I found that locally driving a subset of neurons in the layer II microcircuit at theta frequency with a light stimiulus produced a nested field rhythm at gamma frequency that was also evident as rhythmic inhibition onto stellate cells. Critically, these interacting rhythms closely resembled those recorded from behaving animals. In addition, I found that this thetanested gamma is sufficiently regular to act as a clock-like reference signal, indicating its potential role in implementing a timing code. To functionally dissect the circuit I performed multiple simultaneous whole-cell patch-clamp recordings during circuit activation. These recordings revealed how feedback interactions between stellate cells and fast-spiking interneurons underpin the theta-nested gamma rhythm. Together, these results suggest that feedback inhibition in layer II acts as a common substrate for theta-nested gamma oscillations and possibly also grid firing fields, thereby providing a framework for understanding how computations are carried out in layer II of the medial entorhinal cortex

Effects of intrinsic neuronal properties in neural dynamics

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, septiembre de 201