In the present study, electrophysiological and behavioral effects of compromised Gama-Aminobutyric Acid (GABAergic) transmission were investigated in adult Rhesus macaque monkeys (N=2). GABAergic transmission was perturbed in the putamen by administration of a GABAa receptor antagonist, gabazine (10 and 500 μM), via a microdialysis-local field potential (MD-LFP) probe. Resultant changes in striatal local field potentials (LFPs) were measured as an assay of synchrony. Gabazine perfusion evoked discrete large amplitude spikes in LFPs in all subjects, and the frequency and shape of individual spikes were concentration-dependent. Pre-treatment with the GABAa receptor agonist, muscimol (100 μM) blocked the gabazine-induced events, confirming a role for GABAa receptors in the effects. Behavioral manifestations of gabazine treatment were observed only at the maximum concentration. Unusual facial movements suggested aberrant electrical activity was propagated from striatum to motor cortex, perhaps via reentrant circuits. These results support a role for GABAergic transmission in segregation of striatal circuits.

While single neurons in inferior temporal (IT) cortex show differential responses to distinct complex stimuli, little is known about the responses of populations of neurons in IT. We recorded single electrode data, including multi-unit activity (MUA) and local field potentials (LFP), from 618 sites in the inferior temporal cortex of macaque monkeys while the animals passively viewed 78 different pictures of complex stimuli. The LFPs were obtained by low-pass filtering the extracellular electrophysiological signal with a corner frequency of 300 Hz. As reported previously, we observed that spike counts from MUA showed selectivity for some of the pictures. Strikingly, the LFP data, which is thought to constitute an average over large numbers of neurons, also showed significantly selective responses. The LFP responses were less selective than the MUA responses both in terms of the proportion of selective sites as well as in the selectivity of each site. We observed that there was only little overlap between the selectivity of MUA and LFP recordings from the same electrode. To assess the spatial organization of selective responses, we compared the selectivity of nearby sites recorded along the same penetration and sites recorded from different penetrations. We observed that MUA selectivity was correlated on spatial scales up to 800 m while the LFP selectivity was correlated over a larger spatial extent, with significant correlations between sites separated by several mm. Our data support the idea that there is some topographical arrangement to the organization of selectivity in inferior temporal cortex and that this organization may be relevant for the representation of object identity in IT.

In this thesis, we classify up to conjugacy the maximal elliptic toral subgroups of all special orthogonal groups SO(V), where (q,V) is a 4-dimensional quadratic space over a non-archimedean local field of odd residual characteristic. Our parameterization blends the abstract theory of Morris with a generalization of the practical work performed by Kim and Yu for Sp(4). Moreover, we compute an explicit Witt basis for each such torus, thereby enabling its concrete realization as a set of matrices embedded into the group. This work can be used explicitly to construct supercuspidal representations of SO(V).

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第23975号 / 人博第1027号 / 新制||人||242(附属図書館) / 2022||人博||1027(吉田南総合図書館) / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)教授 田部 勢津久, 教授 吉田 寿雄, 教授 中村 敏浩, 教授 田中 勝久 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

Local field potentials (LFPs), extracellular potentials that reflect localized electrical activity, have long been used as a window to understand the behavioural dependence and mechanistic aspects of brain physiology. A principal premise that has driven the interpretation of LFPs is that they largely reflect the synaptic drive that impinges on neurons located in the vicinity of the recording microelectrode. An implicit, yet critical, assumption that led to the emergence of this premise is that dendrites, the structures onto which most synaptic inputs project, are purely passive compartments. However, there is a growing body of evidence demonstrating that dendrites express a plethora of active conductance, like voltage-gated ion channels, several of which are active in the subthreshold regime. These subthreshold-activated ion channels and their intra-neuronal localization profiles play widely acknowledged regulatory roles in the physiology, plasticity and pathophysiology of synapses and neurons. Despite this, the implications for the existence of these subthreshold conductances on constituent oscillatory patterns in LFPs and on the phase of neuronal spiking with reference to oscillating LFPs have surprisingly remained unexplored. The aim of this thesis is to examine if there exists a role of subthreshold conductances in regulating LFPs and the phase of spikes with reference to these LFPs. To address this, we chose to study LFPs and spikes from the CA1 region of the rat hippocampus, with hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels forming the specific subthreshold conductance of focus. The reasons behind these choices were manifold. First, CA1 pyramidal neurons are arranged in a laminar open-field configuration, making the interpretation of the source-sink formation in this region relatively tractable. Second, the dendrites of these neurons are endowed with a multitude of subthreshold conductances whose expression profiles, physiology and plasticity have been characterized in great detail. Third, this brain region has been implicated in coding for episodic and spatial memories. The phase of the spikes of the CA1 pyramidal neurons, with reference to the LFP, is believed to serve as a code that can be used to decode the location of the animal. Given that the most dominant LFP pattern seen in the CA1 region during such active exploration (and possibly encoding of spatial memories) consists of oscillations in the 4–10 Hz theta frequency band, we decided to focus our study on theta-frequency LFPs. Finally, consistent with the choice of the specific band of LFP frequencies, we focused on HCN channels because of their predominantly dendritic expression and their ability to bestow resonance and impedance phase lead, both in the theta-frequency range, on CA1 pyramidal neurons. In exploring the role of HCN channels on LFPs, we used a multi-compartmental morphologically realistic CA1 pyramidal neuron model and introduced an HCN channel conductance gradient that was constrained with several experimental measurements. This neuron was driven by dendritic excitatory synapses and perisomatic inhibitory synapses, both theta-modulated with a phase difference of +60º between their arrivals timings. We increased the excitatory synaptic conductance with distance from the soma to account for the fact that irrespective of the location of the synapse in the dendrites, the unitary excitatory post-synaptic potential remains the same at the soma. Employing these model configurations, we generated 25 different synaptic distributions on the same neuronal morphology to account for the input variability and for each of these models, we recorded transmembrane currents from all the compartments, for 8–10 cycles of the theta-modulated inputs. To model LFPs using the forward modelling scheme of line source approximation, we designed a cylindrical neuropil of 40 µm height and 100 µm radius and inserted a virtual linear electrode with 7 contact points distributed on the probe at the canter of the neuropil such that we could compute the LFP at all the strata of the CA1 region. Accounting for the volume of the neuropil and the density of neurons in this region, we took 440 instances of the morphology, rotated them at uniformly distributed angles, and distributed the somata of these model neurons within the neuropil. Each of these 440 neurons received transmembrane currents from one of the 25 models picked uniformly. With a passive model, where we did not introduce HCN channels, we expectedly observed the formation of a source-sink structure that expressed as a progressive phase shift spanning different strata, owing to the perisomatic inhibitory currents coupled with the dendritic excitatory currents. On introducing a somatodendritic gradient of HCN conductance with identical input conditions, we observed a phase lead in the LFPs across all the layers, with the magnitude of the lead increasing with distance from the soma in a manner that was correlated with the increase in HCN conductance. Next, we computed spike phases, for each of the 25 neuron models, with reference to the stratum pyramidale (SP) LFP for model configurations with and without HCN channels. We found that the spikes showed a phase lag in the presence of a gradient of HCN channels when compared to the spike phases measured from the passive neuron models. Finally, we computed the coherence of spikes across all the 25 passive or 25 active (with HCN channels) neuron models and found that the presence of HCN channels greatly enhanced spike phase coherence across neurons. Together, these results demonstrate that the presence of HCN channels introduces a lead in the theta-frequency LFP phase, a lag in the associated spike phase, and a significant enhancement of spike phase coherence. Exploring the robustness of these findings to the model configuration, we first found these conclusions to be robust to increases in neuropil size (400-µm diameter neuropil with 1797 neurons, and 1-mm diameter neuropil with 11297 neurons). Next, we introduced heterogeneities in the population of neurons (in terms of morphology as well as passive and active properties) that formed the neuropil, and found our conclusions to be invariant to such degeneracy in the underlying neuronal population. It has been observed that under certain pathological conditions like epilepsy, an entire population of CA1 neurons can undergo intrinsic plasticity, such as global (i.e., across the entire neuronal topograph) downregulation of HCN channels. To assess the impact of such up/downregulation on LFPs, we respectively increased/decreased HCN channel conductance globally in our model neurons, and found the magnitude of the lead in the LFP phase to progressively increase with HCN-channel conductance. Similarly, the magnitude of the spike-phase lag and the spike phase coherence also progressively increased as functions of HCN-channel conductance. Although such population-level global intrinsic plasticity is observed under pathological conditions, a more physiological scenario would be when a single neuron, in the process of encoding new inputs (such as encoding spatial or episodic memories), undergoes intrinsic plasticity. To assess this, we increased or decreased HCN-channel conductance specifically in a single neuron placed closest to the electrode, while leaving the HCN expression in other neurons of the neuropil at the baseline level. Expectedly, we did not find significant changes in LFP amplitude or phase, but we did find a significant lag in the spike phase preference of the neuron that underwent an upregulation of HCN conductance. Another physiological scenario is when the rat experiences a reward or exhibits anxiety-like behaviour, which can lead to changes in hormonal or neuromodulator concentrations. These changes, functioning through the activation of G-protein coupled receptors and the consequent elevation of cytosolic cyclic adenosine monophosphate (cAMP) concentrations, could shift the half-maximal activation voltage ( V1/2 ) of HCN channels to a more depolarized potential. Would such a shift in V1/2 impact LFPs and spike phases in a manner similar to that observed with increasing the conductance of HCN channels? Assessing this within our modeling framework, we found that shifting the V1/2 by +5 mV resulted in an increased lead in the LFP phase, an increased lag in the spike phase and an enhanced spike phase coherence compared to the case with a hyperpolarized V1/2 . What are the biophysical mechanisms that underlie these robust changes observed in LFPs and spike phases observed as a consequence of these several ways of increasing the current through HCN channels? We reasoned that our observations could be explained by one of the two distinct changes conferred on CA1 pyramidal neuron physiology by the presence of HCN channels. First, in the presence of HCN channels, the voltage response of CA1 pyramidal neurons shows a phase lead with reference to a sinusoidal current input (inductive phase lead) in the theta frequency range. Second, HCN channels regulate the excitability of these cells by decreasing the input resistance and impedance amplitude. To delineate the differential role of the inductive changes vs. changes in excitability, we replaced HCN channels by a faster variant (HCNFast) such that neuronal excitability remained the same while abolishing the inductive phase lead in the theta band. On doing so, we found that the lead in the LFP phase and the lag in the spike phase brought about by HCN channels was partially reversed when HCN conductance values were low. However the reversal was not substantial when HCN conductance values were high, suggesting that the inductive phase component dominates at lower HCN channel conductances, whereas the excitability component plays a critical role at higher HCN conductances. Akin to intrinsic plasticity mentioned above, under certain pathological conditions, an entire population of neurons can undergo scaling of their excitatory or inhibitory synapses. In assessing the implications for such synaptic plasticity, we first found that our conclusions on the roles of HCN channels in introducing a lead in the LFP phase, a lag in the spike phase and an enhancement of spike phase coherence were invariant to the specific values of synaptic conductances, or the phase difference between excitatory and inhibitory theta-modulated inputs. While these observations further established the robustness of the changes brought about by HCN channels to LFPs and associated spikes, we next asked whether synaptic plasticity, mediated by changes in subthreshold synaptic conductances, could itself bring about changes in the LFP and spike phase. Expectedly, we found that scaling up of excitatory synapses introduced a mild lag in the LFP phase and a lead in the spike phase, whereas scaling up of inhibitory synapses introduced a lead in the LFP phase and a lag in the spike phase. Finally, we observed a critical role of the arrival phase of inhibition with reference to excitation in altering both, the stratum pyramidale LFP and associated spike phases, with the magnitude of change in both the LFP and the spike phase roughly following the magnitude of the shift in the excitatory-inhibitory phase difference. However, in contrast to changes observed with HCN-channel plasticity, there was no significant change in spike phase coherence with any of the three forms of synaptic changes explored. Together, our results identify definite roles for HCN channels and synaptic receptors in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies and present a clear case for the incorporation of subthreshold-activated ion channels, their gradients, and their plasticity into the computation of LFPs. Given the rich expression of several subthreshold ion channels — including HCN, A-type potassium and T-type calcium — in neuronal dendrites, future work could focus on the impact of subthreshold channels on LFPs recorded in different brain regions under different behavioral states. This thesis is organized into seven chapters. Chapter 1 provides the motivations for the study, introduces the aim of the study and poses the specific questions asked in our endeavor to understand the role of subthreshold conductances in regulating LFPs and spike phases. Chapter 2 discusses the physiological foundations and relevant literature that places the questions posed in the first chapter in the context of the aim of the thesis, with an emphasis on the literature on HCN channels. In chapter 3, we introduce the computational and theoretical foundations required to model neurons and to compute LFPs. In chapter 4, we look at the consequences of the presence of a non-uniform density of somatodendritic HCN channels on LFPs and spike phase and test the robustness of the effects observed. In chapter 5, we present our assessment of the impact of intrinsic plasticity/modulation of HCN channels on LFPs and spike phases, also exploring the biophysical mechanisms underlying such an impact. In chapter 6, we test if the observed effects still hold under synaptic plasticity, and assess the regulation of LFPs and spike phases by synaptic changes. In chapter 7, we summarize and conclude the results presented in the preceding chapters and provide some potential directions for future studies.

<p>Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.</p> / Dissertation

How is information processed in the brain? This is one of the main and most challenging questions in Neuroscience. The established hypothesis is that information is encoded in the temporal dynamics of spikes. However, there is growing evidence that continuous signals such as Local Field Potentials (LFP) can play an important role in coding neural information. Recently, Montemurro et al. [2008] reported that the phase-of-firing code, a mechanism previously observed in the hippocampus, is used in the sensory cortices for information encoding. In the phase-of-firing code, the neurons communicate spikes with respect to the phase of continuous signals produced by population activity, such as the LFP. Using information-theoretic measures, it was shown that when the timing of spikes was measured with respect to the phase of the LFP, an extra amount of sensory information was revealed in the responses that was not available from the spike codes alone. On the one hand, it still remains to be established how widespread this novel coding mechanism is. So far it has been verified in a few sensory modalities and it is not clear whether it is a universal coding mechanism. On the other, the estimation of information from continuous signals poses serious challenges from a technical point of view. The main reason is that accurate estimations of information measures require unrealistic amount of experimental data, mostly due to the presence of correlated activity. When these measures are applied to assess the information content in continuous responses, they lead to severe biases in the results, which can affect the conclusions regarding the validity of specific neural codes. The main goal of this Thesis is to explore the universality of the phase-of-firing code by studying it in novel systems, establish the origin of this code, and to develop more effcient numerical methods to accurately quantify information encoded in continuous brain signals. In particular, in this Thesis we investigate the role of continuous signals in sensory modalities where it has not been explored so far. We verified the presence of a phase-of-firing code in both the somatosensory cortex of the rat, and the visual thalamus of mice, thus giving support to the possible universality of this coding mechanism. While the phase-of-firing code found in these systems shares common features with those found in previous studies, we also characterised important differences. In the rat whisker system it was found that high frequency bands of the LFP play a more prominent role than that observed in the visual and auditory cortices of monkeys. This is compatible with the behavioural and mechanical constraints of this system, which require a high discrimination of finely structured temporal information in the stimulus. In the case of the visual thalamus of mice, we found that the phase-of-firing code contributes significantly to the encoding of irradiance information conveyed by melanopsin photoreceptors in the retina. We also investigated the source of the phase-of-firing codes in cortex by modelling the relationship between population spikes and LFP. In particular, we studied the interplay between the effective spatial integration of information resulting from population activity and the temporal memory imprinted in the LFP as a consequence of filtering mechanisms in the neural tissue. We found that most of the information in the LFP comes from a neural neighbourhood of a radius of about 150-350 μm, and a temporal history of 200-300 msec. Finally, we developed novel practical methods for quantifying the information content of continuous signals in the brain, which yield accurate results under realistic experimental conditions. These methods are based on the projection of the statistics of the response space into a lower dimensional manifold. In particular, we modelled continuous neural responses as a hierarchy of Markov models of increasing order, and found that the structure of temporal dependencies of real LFP can be captured by the lowest orders. This helped us put a new light on the previous studies regarding the phase-of-firing code. Altogether, these results contribute an advance both at the level of understanding information coding strategies combining spike and continuous signals, and the required computational methods to quantify accurately information in experimental neural responses.

No description available.

No description available.

Brain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms. / text