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.

<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

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.

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

abstract: The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses. / Dissertation/Thesis / Doctoral Dissertation Neuroscience 2019

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Large scale functional brain networks, defined by synchronized spontaneous oscillations between spatially distinct anatomical regions, are essential to brain function and have been implicated in disease states, cognitive capacity, and many sensing and motor processes. In this work, we sever the corpus callosum in the rodent model to determine if structural connectivity (specifically the primary interhemispheric pathway) organizes and influences bilateral functional connectivity and brain-wide spatiotemporal dynamic activity patterns. Prior to the callosotomy work, resting state brain networks were evaluated using blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) magnetic resonance imaging contrast mechanisms, and revealed that BOLD and CBV provide highly similar spatial maps of functional connectivity; however, the amplitude of BOLD connectivity was generally stronger. The effects of extended anesthetic durations on functional connectivity were also evaluated revealing extended isoflurane anesthetic periods prior to the switch to dexmedetomidine attenuates functional activity for a longer duration as compared to a shorter isoflurane paradigm. We also observed a secondary significant evolution of functional metrics occurring during long durations of dexmedetomidine use under the currently accepted and refined dexmedetomidine sedation paradigm. Taking these previous findings into account, we moved forward with the callosotomy study. Functional network integrity was evaluated in sham and full callosotomy groups using BOLD and electrophysiology. Functional connectivity analysis indicated a similar significant reduction in bilateral connectivity in the full callosotomy group as compared to the sham group across both recording modalities. Spatiotemporal dynamic analysis revealed bilaterally symmetric propagating waves of activity in the sham data, but none were present in the full callosotomy data; however, the emergence of unilateral spatiotemporal patterns became prominent following the callosotomy. This finding suggests that the corpus callosum could be largely responsible for maintaining bilateral network integrity, but non-bilaterally symmetric propagating waves occur in the absence of the corpus callosum, suggesting a possible subcortical driver of the dynamic cascading event. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks.

Le temps est une dimension essentielle de la vie. Il est nécessaire, entre autres, pour réaliser des mouvements coordonnés, pour communiquer, mais aussi dans la prise de décisions. L’objectif principal de cette thèse était de caractériser le rôle d’un réseau amygdalo-préfronto-dorsostriatal dans la mémorisation et l’encodage du temps chez le rat. Dans un premier temps, nous avons décrit le comportement temporel du rat lors d’une tâche de suppression conditionnée (i.e. la suppression d’une réponse instrumentale d’appui sur levier par la présentation d’un son associé à un stimulus aversif), démontrant ainsi un contrôle temporel fin du comportement dans une situation Pavlovienne aversive. Dans un deuxième temps, nous avons analysé les potentiels de champs locaux (analyse fréquentielle des activités oscillatoires) de notre réseau d’intérêt au début d’un apprentissage associatif et après surentraînement dans la tâche de suppression conditionnée. En effet, le comportement temporel moteur nécessite un grand nombre de séances d’apprentissage pour devenir optimal, alors que l’apprentissage temporel est, lui, très rapide. Cette étude nous a permis de caractériser des corrélats neuronaux temporels au sein de ce réseau, que ce soit au niveau des structures individuelles ou au niveau de l’interaction entre ces structures. De plus, ces corrélats neuronaux sont modifiés selon le niveau d’entraînement des animaux. Enfin, dans une troisième étude, nous avons démontré que des ratons juvéniles (pré-sevrage), qui présentent un cortex préfrontal ainsi qu’un striatum dorsal immatures, peuvent mémoriser et différencier des intervalles de temps, ouvrant donc la question sur le rôle de ce réseau dans l’apprentissage temporel au cours du développement. / Time is an essential dimension of life. It is necessary for coordinating movement, for communication, but also for decision-making. The principal goal of this work was to characterize the role of an amygdalo-prefronto-dorsostriatal network in the memorization and encoding of time in a rat model. Firstly, we described temporal behavior in a conditioned suppression task (i.e. the suppression of an instrumental lever-pressing response for food by the presentation of a cue associated with an aversive event), therefore showing a precise temporal control in Pavlovian aversive conditioning. Secondly, we measured local field potentials in our network of interest at the beginning of associative learning and after overtraining in the conditioned suppression task. In effect, motor temporal behavior requires a large number of training sessions to become optimum, but temporal learning happens very early in training. This study allowed us to characterize, using frequency analysis of oscillatory activities, neuronal correlates of time in this network both at the level of individual structures, but also in their interactions. Interestingly, these neural correlates were modified by the level of training. Finally, we demonstrated that juvenile rats (pre-weaning), with an immature prefrontal cortex and dorsal striatum, can memorize and discriminate temporal intervals, raising questions on the role of this amygdalo-prefronto-dorsostriatal network in temporal learning during development.

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