The human brain relies on the complex interactions of distinct brain regions to support cognitive processes. The interplay between the hippocampus and neocortical regions plays a key role in the formation, storage, and retrieval of long-term episodic memories. Oscillatory activities during sleep are a fundamental mechanism that binds distributed neuronal networks into functionally coherent ensembles. However, the large-scale hippocampal-neocortical oscillatory mechanisms that support flexible modulation of long-term memory remain poorly understood. Furthermore, alterations to physiologic spatiotemporal patterns that are essential for intact memory function can result in pathophysiology in brain disorders such as focal epilepsy. Investigating how epileptic network activity disrupts connectivity in distributed networks and the organization of oscillatory activity are additional crucial areas that require further research. Our experiments on rodents and human patients with epilepsy have provided valuable insights into these mechanisms. In rodents, we used high-density microelectrode arrays in tandem with hippocampal probes to analyze intracranial electroencephalography (iEEG) from multiple cortical regions and the hippocampus. We identified key hippocampal-cortical oscillatory biomarkers that were differentially coordinated based on the age, strength, and type of memory. We also analyzed iEEG from patients with focal epilepsy and demonstrated how individualized pattern of pathologic-physiologic coupling can disrupt large-scale memory circuits. Our findings may offer new opportunities for therapies aimed at addressing distributed network dysfunction in various neuropsychiatric disorders.

As theoretical neuroscience has grown as a field, machine learning techniques have played an increasingly important role in the development and evaluation of theories of neural computation. Today, machine learning is used in a variety of neuroscientific contexts from statistical inference to neural network training to normative modeling. This dissertation introduces machine learning techniques for use across the various domains of theoretical neuroscience, and the application of these techniques to build theories of neural circuits. First, we introduce a variety of optimization techniques for normative modeling of neural activity, which were used to evaluate theories of primary motor cortex (M1) and supplementary motor area (SMA). Specifically, neural responses during a cycling task performed by monkeys displayed distinctive dynamical geometries, which motivated hypotheses of how these geometries conferred computational properties necessary for the robust production of cyclic movements. By using normative optimization techniques to predict neural responses encoding muscle activity while ascribing to an “untangled” geometry, we found that minimal tangling was an accurate model of M1. Analyses with trajectory constrained RNNs showed that such an organization of M1 neural activity confers noise robustness, and that minimally “divergent” trajectories in SMA enable the tracking of contextual factors. In the remainder of the dissertation, we focus on the introduction and application of deep generative modeling techniques for theoretical neuroscience. Specifically, both techniques employ recent advancements in approaches to deep generative modeling -- normalizing flows -- to capture complex parametric structure in neural models. The first technique, which is designed for statistical generative models, enables look-up inference in intractable exponential family models. The efficiency of this technique is demonstrated by inferring neural firing rates in a log-gaussian poisson model of spiking responses to drift gratings in primary visual cortex. The second technique is designed for statistical inference in mechanistic models, where the inferred parameter distribution is constrained to produce emergent properties of computation. Once fit, the deep generative model confers analytic tools for quantifying the parametric structure giving rise to emergent properties. This technique was used for novel scientific insight into the nature of neuron-type variability in primary visual cortex and of distinct connectivity regimes of rapid task switching in superior colliculus.

As animals explore and interact with their surroundings, information about their environment is constantly processed from sensory stimuli into perception. This information informs their behavior, decision-making, and understanding of their world. Information processing and perception have long been thought to be modulated by the behavior state of the animal (Cano et al. 2006; Niell and Stryker 2010; Polack et al. 2013; Poulet & Petersen, 2008; Briggs 2013, Schölvinck et al. 2015). Previous research has shown that behavior state strongly correlates with perceptual performance in a sensory discrimination task in rodents (McGinley et al. 2015, Schriver et al., 2018). However, the neural correlates behind this modulation of perception, information processing, and behavioral performance are not yet fully understood. The first part of this work investigates the relationship between cell-type specific spontaneous cortical activity and behavior state as defined by pupil-linked arousal. Spontaneous activity is essential in understanding the link between behavior state and information processing as it serves as the baseline state of activity prior to processing any stimuli information. Within the sensory cortices, excitatory and inhibitory neurons work in unison to dictate network activity. Three main classes of cortical inhibitory neurons are somatostatin-expressing neurons (SST), vasointestinal peptide-expressing neurons (VIP), and parvalbumin-expressing neurons (PV). These four cell types comprise the VIP disinhibitory circuit, in which VIP neurons disinhibit excitatory neurons by inhibiting PV and SST neurons. PV and SST neurons directly inhibit excitatory cells, so by suppressing their activity VIP neurons indirectly disinhibit excitatory cells. This circuit is a vitally important system used to modify excitatory activity in all cortical regions. The spontaneous activity of excitatory neurons and three classes of inhibitory neurons (somatostatin-expressing neurons (SST), vasointestinal peptide-expressing neurons (VIP), and parvalbumin-expressing neurons (PV)) was individually examined in this study. To visualize in-vivo spontaneous cortical activity, a genetically encoded calcium indicator (GCaMP) was expressed in the somatosensory cortex, and the population-level neural activity was imaged using fiber photometry. Despite the relationship between these neurons as defined by the VIP disinhibitory circuit, the spontaneous activity of excitatory, VIP, PV, and SST neurons was found to positively correlate with pupil size for all of these neuron types. This supports the theory that VIP and other interneuron types may be active in various functions, not just the disinhibition of excitatory cells. Pupil-evoked activity, or spontaneous activity during highly aroused states, was also found to positively correlate with pupil size for all cell types and had the strongest correlation for all correlation types. Therefore, pupil-linked arousal level relates to the increased activity of both excitatory and inhibitory cortical cells. While the first chapter focuses on spontaneous activity, the second focuses on stimulus-evoked activity. Stimulus-evoked activity in the somatosensory pathway can be caused by both internally generated stimuli and external stimuli. In the first step of sensory processing, the sensory receptors cannot distinguish between these two types of stimuli. However, the differentiation between the two is necessary in order to distinguish self from non-self. The motor-related timing signals that influence sensory processing and enable distinction between internally generated and external stimuli is termed corollary discharge. Where and how the mechanism of corollary discharge occurs in the somatosensory system is not well understood. To investigate corollary discharge in the somatosensory system, the neural activity in the somatosensory cortex was analyzed during internally generated stimuli and during delivery of external stimuli. More specifically, the activity in the vibrissa somatosensory cortex of rodents during self-induced whisking and during delivery of an air puff to the whiskers was examined. In the primary and secondary somatosensory cortex, excitatory activity was inhibited just prior to whisking and suppressed to a lower level during whisking in comparison to the activity level during air puff delivery. The three main classes of inhibitory neurons were studied to explore the possibility of local inhibition causing this suppression of the excitatory signal during whisking. VIP, PV and SST neurons all exhibited a similar pre-whisking inhibition and suppression of activity during whisking, eliminating the possibility of their role in pre-whisking inhibition and whisking activity suppression. Other regions involved in the somatosensory pathway and sensorimotor processing, such as the thalamus and motor cortex, were also found to not contribute to pre-whisking inhibition or whisking activity suppression as they were also found to exhibit the same phenomenon. After ruling out cortical inhibitory neurons and somatosensory regions in the involvement of corollary discharge, external higher-order regions were investigated. Previous studies on the sources of corollary discharge in the cerebellum have shown corollary discharge signals originate from coordination of several different higher-order brain regions (Person A., 2019). To determine these potential regions for somatosensory corollary discharge, viral tracing vectors were used to locate regions with long-range inhibitory projections to the somatosensory cortex. The globus pallidus (GP) was first investigated due to its role in voluntary movement and projections to the frontal cortex (Saunders et al. 2015). However, no inhibitory projections from the GP to the somatosensory cortex were found. The striatum, which is mainly GABAergic (and therefore inhibitory), also seemed to be a likely candidate. Preliminary tracing results suggest the striatum does have inhibitory projections to the somatosensory cortex. Further studies of both retrograde and anterograde tracing must be performed to confirm this finding. Nonetheless, the evidence of corollary discharge as seen through pre-whisking inhibition and the suppression of activity during whisking in S1, S2, thalamus, and motor cortex is a novel finding and opens up many avenues for further research.

Complex genetic mechanisms both endow developing neuronal subtypes with distinct molecular identities and translate those identities into the signatures of cell surface axon guidance molecules that direct neural circuit assembly. The final steps of this process, where axon guidance molecules instruct circuit outcomes, are well-understood. However, the upstream identity molecules that define guidance molecule signatures, and the molecular mechanisms by which cell type identity is transformed into these signatures, remain enigmatic. The murine olfactory system contains nearly 1,5000 olfactory sensory neuron (OSN) subtypes which are intermixed in the olfactory epithelium (OE). Each OSN subtype expresses a unique olfactory receptor (OR) protein which both tunes its response properties to odorants in the environment and acts as an identity molecule that ensures all axons of a given OSN type converge to a single set of target glomeruli in the olfactory bulb (OB). Using a combination of bioinformatic and mouse genetic approaches, we have discovered an unanticipated role for endoplasmic reticulum stress (ER stress) and the unfolded protein response (UPR) in the translation of OR identity to OSN axon guidance molecule expression and glomerular targeting. We find that slight differences in OR amino acid sequences lead to differential activation of the ER stress sensor PERK in different OSN subtypes. Graded patterns of the UPR are then interpreted through a master regulator transcription factor, Ddit3, which controls a set of stress-responsive axon guidance molecules that orchestrate the process of glomerular segregation in the OB. Our results define a novel paradigm for axon guidance in which graded activation of a canonical stress response pathway is leveraged towards the conversion of discrete neuronal identities into discrete circuit formation outcomes. These findings may be widely relevant for the formation of neural circuits across a variety of systems.

Escape behavior is the critical output of rapid sensorimotor processing in the brain that allows animals to sense danger and avoid it. The circuit structures and mechanisms that underlie escape are still under investigation. Drosophila larvae are an advantageous system for studying the neuromuscular circuitry of escape behavior. When threatened with harmful mechanical touch, heat, or light, larvae perform C-shaped bending and lateral rolling, followed by rapid forward crawling. The sensory input and neural circuitry that promotes escape in the larva have been extensively characterized, but we do not understand how bending and rolling motor programs are generated by the larval neuromuscular system. This work identifies the movement patterns, muscle activities, and motor circuit features that drive escape behavior. High-speed imaging approaches reveal that larvae select between four distinct, interchangeable patterns of escape rolling, and that each pattern consists of synchronous rotations of every segment as the larva rotates. Investigating electron microscopic reconstructions of premotor and motor neurons elucidates premotor to motor connectivity patterns that could underlie sequential muscle activity that circumnavigates the larva and propels synchronous rotation along the whole body. Volumetric Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy uncovers that, unlike larval crawling, a well-studied form of larval locomotion that is driven by bilaterally symmetric peristaltic waves of muscle activity, the muscle activity during bending and rolling occurs in a circumferential sequence that is synchronous along the larva’s segments. Muscles neighboring the dorsal and ventral midlines of the larva demonstrate left-right symmetric activity during rolling, and ventral muscles appear to drive the propulsion. Shifts in magnitude of left-right symmetric activity in midline muscles allow the larva to transition from initial escape bending into escape rolling. Preliminary computational predictions of PMN activities confirm the likely necessity of strong ventral muscle coactivity for driving escape. Probing specific PMNs during rolling demonstrates robustness of circuits controlling escape and requires further investigation, alongside the role that sensory feedback could play in this behavior. Altogether, these data reveal a new circuit organization and motor activity pattern that underlie the coordination of muscles during an escape sequence. Future work could reveal circuit components necessary for escape, including the mechanistic basis for action selection, behavioral maintenance, and behavioral flexibility.

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The limbic system is hypothesized to play a critical role in pathophysiology of schizophrenia, with abnormalities thought to contribute to the expression of various aspects of the cognitive deficits and clinical symptoms. Psychosis is understood as highly heritable and family members, specifically non-affected siblings, while not displaying overt signs of the disorder, often exhibit features similar to those observed in patients, though to a lesser degree. The overarching aim of this project was to investigate the integrity of limbic circuitry in a sample of patients with schizophrenia and their non-affected siblings and examine its potential relationship with various clinical features of the illness. Cortical thickness of the entorhinal, parahippocampal, cingulate, and orbitofrontal cortices; as well as subcortical surface shape of the hippocampus and amygdala were the focus of this study. Findings from this study reveal relative similarity in limbic integrity between individuals with schizophrenia and theirnon-affected siblings, which are both disparate from healthy individuals. This suggests aspects of the neurobiological underpinnings of psychosis, particularly limbic regions, are genetically influenced regardless of symptom expression and are latent features in non-affected family members. Relationships between positive symptomatology and shape abnormalities of subcortical structures suggest a potential substrate for clinical characteristics in psychosis not evident in non-ill siblings.

Magnetic sensors are widely used in the field of mineral, navigational, automotive, medical, industrial, military, and consumer electronics. Many magnetic sensors have been developed that are generated by specific laws or phenomena: such as search-coil, fluxgate, Hall Effect, anisotropic magnetoresistance (AMR), giant magnetoresistance (GMR), magnetoelectric (ME), magnetodiode, magnetotransictor, fiber-optic, optical pump, superconducting quantum interference device (SQUID), etc. Each of these magnetic field sensors has their merits and application areas. For low power consumption (<10uW), quasi-static frequency (<10Hz) and high sensitivity (<nT) application, magnetoelectric laminate sensors offer the best potential capability and thus are the topic of my dissertation. Here, in this thesis, I have focused on designs and optimizations of magnetoelectric sensor units (i.e., sensors and circuit). To achieve my goals, I have developed some useful rules for ME sensor and detection circuit design. For ME sensor optimization, designs should consider both frequencies far away from resonance and at resonance. For the former one, both internal and external noise contribution must be considered, as one of them will limit practical applications. With regards to the internal noise sources, I have developed two design optimization methods, designated as ”'scale effect” and “ME array”. I showed that they have the ability to increase the magnetic field detection sensitivity, which was verified by experiments. With regard to external noise consideration, I have investigated how the fundamental extrinsic noise sources (temperature fluctuation, vibration, etc) affect ME laminate sensors. A concept of separating signal and noise modes into difference is put forward. Optimization with this concept in mind required us to redesign the internal structure of ME laminate sensors. At the resonant frequency, the ME voltage coefficient α_ME is the most important parameter. To enhance resonant gain in α_ME, I have developed a three phase laminate concept, which is based on increasing the effective mechanical factor Q while reducing the resonant frequency. A ME voltage coefficient of α_ME ~40V/cm.Oe has been achieved at resonance, which is about 2x higher than that of a conventional bending mode. Investigations of detection circuit optimization were also performed. Component selection strategies and a new charge topology were considered. Proper component values were required to optimize the charge detection scheme. It was also found, under some specific conditions to satisfy the circuit stability, that if the lowest required measurement frequency of the charge source was f1, then that it was not necessary to make the high corner frequency <i>f</i>_p of the charge amplifier lower than <i>f</i>₁: as doing so would decrease the system's signal-to-noise ratio (SNR). A high pass, high order filter placed behind the charge amplifier was found to increase the charge sensitivity, as it narrows the intrinsic noise bandwidth and decreases the output noise contribution, while only slightly affecting the signal's output amplitude. Prototype ME unit were also constructed, and their noise level simulated by Pspice. Experimental results showed that prototypes ME unit can reach their detection limit. In addition, a new magneto-electric coupling mechanism was also found, which had a giant ME effect. / Ph. D.