Indiana University-Purdue University Indianapolis (IUPUI) / Mental health is crucially linked to social behavior. A crucial aspect of healthy social behavior involves learning to adapt emotional responses to social cues, for example learning to suppress anxiety through social familiarity, or social familiarity induced anxiolysis (SoFiA). SoFiA is well documented; however, the neural mechanisms of SoFiA are unclear. SoFiA is modeled in rats by employing a social interaction habituation (SI-hab) protocol. Using SI-hab protocol it has been determined that SoFiA represents social safety learning, which requires both anxiogenic stimulus (Anx) and social familiarity (SF) during training sessions (5-6 daily SI sessions), and SoFiA expression is dependent on infralimbic cortex (IL). Based on these findings we hypothesize that Anx and SF are processed by unique neural systems, and repeated convergence of these signals interact within IL to induce plasticity, resulting in social safety learning and anxiolysis. Following SoFiA expression, rats were either sacrificed 30 minutes {for gene expression or Neural Activity Regulated Gene (NARG) analysis} or perfused 90 minutes (for cFos immunoreactivity analysis) after SI session on social training day 5. This led to gaining insights into regions of brain involved in SoFiA response as well as the underlying molecular mechanisms. We identified amygdala, specifically the central amygdala (CeA), basomedial amygdala (BMA) and basolateral amygdala (BLA) as potential candidate regions in SoFiA response. Next, we investigated the role of IL and its efferent pathways in SoFiA expression using inhibitory DREADDs and intersectional chemogenetics to inhibit IL projection neurons and/or axons. We identified that specific projection neurons within the IL are pivotal for SoFiA expression, and that within these projections, the ones that specifically projected to the amygdala are most crucial for expression of SoFiA. / 2021-07-01

One of two project reports. Part B can be found at: / No abstract was provided. / Thesis / Master of Engineering (ME)

There has been a significant increase in the amount of connectomics data available at the level of single neurons and single synapses in the last few years. This increase enabled investigations into the structure and function of neural circuits in much greater detail than ever before. Thus, the next step in our quest to understand the brain's functional logic is the development of tools and methods to enable us to extract data from and model these new connectomics datasets, and their use to start to examine the brain computationally. Specifically, for Drosophila melanogaster, the fruit fly, a large amount of data on the connectome have become available in the last few years. In this dissertation, we start by introducing the tools we have built to extract information from the Drosophila connectome and to create spiking models of neuropils using this information to model sensory processing and associative learning circuits at single-synapse scale. We then use the toolkit we have introduced to explore sensory processing and associative learning in the brain. First, we introduce FlyBrainLab, an interactive computing environment designed to accelerate the discovery of functional logic of the Drosophila brain. Then, we propose a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain, providing a language not only for modeling circuit motifs but also for programmatically exploring their functional logic; we introduce the FeedbackCircuits library for exploring the functional logic of the massive number of feedback loops (motifs) in the fruit fly brain, and NeuroNLP++, an application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets. Thirdly, following up on the second, we explore the construction of antennal lobe circuits using models of glomeruli. We explore the composability of the connectivity of glomeruli with local neuron feedback loops, and quantitatively characterize the I/O of the AL as a function of feedback loop motifs in the one-glomerulus, two-glomerulus and 23-glomerulus scenarios. Lastly, in the final chapter, we consider the modeling of the mushroom body, a second order olfactory neuropil and a center of associative learning, to demonstrate how the architecture of the circuit interacts with the circuit mechanisms by which sensory inputs are represented and memories are updated. Thus, in this dissertation we introduce an approach for the analysis and modeling of neural circuits based on connectomics data, and apply this approach to neural circuits spanning multiple neuropils to extract and analyze the principles of computation in the brain. The methodology described here is designed to be applied to different sensory systems and organisms to infer the functional logic of connectome-based neural circuits.

The approaches available for unlocking a neural circuit – deciphering its algorithm’s means and ends – are restricted by the biological characteristics of both the circuit in question and the organism in which it is studied. The cerebellum has long appealed to circuits neuroscientists in this regard because of its simple yet evocative structure and physiology. Decades of efforts to validate theories inspired by its distinctive characteristics have yielded intriguing but highly equivocal results. In particular, the general spirit of David Marr and James Albus’s models of cerebellar involvement in associative learning, now almost 50 years old, continues to shape much research, and yet the resulting data indicates that the Marr-Albus theories cannot, in their original incarnations, be the whole story. In efforts to resolve these mysteries of the cerebellum, researchers have pushed the advantages of its simple circuit even further by studying it in model organisms with complimentary methodological advantages. Much early work for example was conducted in monkeys and humans taking advantage of the mechanically simple and precise oculomotor behaviors at which these foveates excel. Then, as genetic tools entered the scene and became increasingly powerful, neuroscientists began porting what had been learned into mouse, a model system in which these tools can be deployed with great sophistication. This was effective in part because cerebellum is highly conserved across vertebrates so complimentary insights can be made across different model systems. Today genetic prowess has been further augmented by rapid advances in optical methods for visualizing and manipulating genetically targeted components. The promise of these new capabilities provides grounds for exploring additional model organisms with characteristics particularly suited to harnessing the power of modern methodology. In the following chapters I explore the promise and challenges of adding a new organism to the current pantheon of most commonly studied cerebellar model organisms. In chapter 1, I introduce the cerebellar circuit and a sampling of the historically equivocal outcomes met by efforts to test Marr-Albus theories in the context of a classical cerebellar learning paradigm: vestibulo-ocular reflex adaptation. In chapter 2, I detail my efforts to establish a method for population calcium imaging in cerebellar granule cells (GCs) of the weakly electric mormyrid fish, gnathonemus petersii. The unusual anatomical placement of GCs in this organism, directly on the surface of the brain, is ideal for optical methods, which require the ability to illuminate structures of interest. Furthermore, in the mormyrid, GCs play analogous role in two circuits -- the cerebellum and a purely sensory structure, the electrosensory lobe, which has a cerebellum-like structure. This latter circuit is unusually well-characterized and appears to employ a Marr-Albus style associative learning algorithm. This could provide a helpful context for interpreting the purpose of GC processing, shared by this circuit and the cerebellum proper. However, taking advantage of these qualities will require overcoming methodological hurdles presented by imaging in this as-yet not genetically tractable organism. While I was able to load and image evoked transients in these cells, and twice observed spontaneous transient, I did not find a loading method that allowed routine observation of spontaneous levels of activity. In chapter 3, I introduce the larval zebrafish, danio rerio, an organism in which optical and genetic methods are already quite established. The zebrafish is genetically tractable and orders of magnitudes smaller than other vertebrate model systems, making it extremely accessible to optical monitoring and manipulation of neural activity. However, in contrast to the mormyrid, very little is known about the physiology of the cerebellar circuit components in this organism or the behaviors to which they contribute. In chapter 4 I detail my efforts to contribute to this modest foundational knowledge by characterizing the electrophysiological activity of Purkinje cells of larval zebrafish during the optomotor response (OMR)—a behavior with similarities to cerebellar-dependent visual stabilization behaviors that have been studied extensively in mammals. I observe a diversity of structured motor and visual activity that suggests that Purkinje cells could contribute to adjusting swim speed during the OMR and other behaviors. In chapter 5, I outline some of the upfront work that remains before cerebellar researchers are likely to fully harness the power of optical and genetic methods in the zebrafish as well as the types of experiments that may become possible if we do.

<div> <div> <div> <p>Process variations and increasing leakage current are major challenges toward memory realization in deeply-scaled CMOS devices. Spintronic devices recently emerged as one of the leading candidates for future information storage due to its potential for non-volatility, high speed, low power and good endurance. In this thesis, we start with the basic concepts and applications of three spintronic devices, namely spin or- bit torque (SOT) based spin-valves, SOT-based magnetic tunnel junctions and the magnetic skyrmion (MS) for both logic and machine learning hardware. </p> <p>We propose a new Spin-Orbit Torque based Domino-style Spin Logic (SOT-DSL) that operates in a sequence of Preset and Evaluation modes of operations. During the preset mode, the output magnet is clocked to its hard-axis using spin Hall effect. In the evaluation mode, the clocked output magnet is switched by a spin current from the preceding stage. The nano-magnets in SOT-DSL are always driven by orthogonal spins rather than collinear spins, which in turn eliminates the incubation delay and allows fast magnetization switching. Based on our simulation results, SOT-DSL shows up to 50% improvement in energy consumption compared to All-Spin Logic. Moreover, SOT-DSL relaxes the requirement for buffer insertion between long spin channels, and significantly lowers the design complexity. This dissertation also covers two applications using MS as information carriers. MS has been shown to possess several advantages in terms of unprecedented stability, ultra-low depinning current density, and compact size. </p><p><br></p><p>We propose a multi-bit MS cell with appropriate peripheral circuits. A systematic device-circuit-architecture co-design is performed to evaluate the feasibility of using MS-based memory as last-level caches for general purpose processors. To further establish the viability of skyrmions for other applications, a deep spiking neural network (SNN) architecture where computation units are realized by MS-based devices is also proposed. We develop device architectures and models suitable for neurons and synapses, provide device-to-system level analysis for the design of an All-Spin Spiking Neural Network based on skyrmionic devices, and demonstrate its efficiency over a corresponding CMOS implementation.</p> <div> <div> <div> <p><br></p><p>Apart from the aforementioned applications such as memory storage elements or logic operation, this research also focuses on the implementation of spin-based device to solve combinatorial optimization problems. Finding an efficient computing method to solve these problems has been researched extensively. The computational cost for such optimization problems exponentially increases with the number of variables using traditional von-Neumann architecture. Ising model, on the other hand, has been proposed as a more suitable computation paradigm for its simple architecture and inherent ability to efficiently solve combinatorial optimization problems. In this work, SHE-MTJs are used as a stochastic switching bit to solve these problems based on the Ising model. We also design an unique approach to map bi-prime factorization problem to our proposed device-circuit configuration. By solving coupled Landau- Lifshitz-Gilbert equations, we demonstrate that our coupling network can factorize up to 16-bit binary numbers. </p> </div> </div> </div> </div> </div> </div>

Deafferented cortical visual areas exhibit topographical plasticity such that their constituent neural populations adapt to the loss of sensory input through the expansion and eventual remapping of receptive fields to new regions of space. Such representational plasticity is most compelling in the long-term (months or years) but begins within seconds of retinal deafferentation (short-term plasticity). The neural mechanism proposed to underlie topographical plasticity is one of disinhibition whereby long-range horizontal inputs are "unmasked" by a reduction in local inhibitory drive. In this dissertation, four experiments investigated the neural mechanisms of short-term visual plasticity and disinhibition in humans using a combination of psychophysics and event-related potentials (ERPs). Short-term visual plasticity was induced using a stimulus-induced analog of retinal deafferentation known as an artifical scotoma. Artificial scotomas provide a useful paradigm for the study of short-term plasticity as they induce disinhibition but are temporary and reversible. Experiment 1 measured contrast response functions from within the boundaries of an artificial scotoma and evaluated them relative to a sham control condition. Changes in the contrast response function suggest that disinhibition can be conceived of in terms of two dependent but separable processes: receptive field expansion and unrestricted neural gain. A two-process model of disinhibition is proposed. A complementary ERP study (Experiment 2) recorded visual evoked potentials elicited by probes appearing within the boundaries of an artificial scotoma. Results revealed a neural correlate of disinhibition consistent with origins in striate and extrastriate visual areas. Experiment 3 and 4 were exploratory examinations of the representation of space surrounding an artificial scotoma and revealed a neural correlate of invading activity from normal cortex. Together, the results of these four studies strengthen the understanding of the neural mechanisms that underlie short-term plasticity and provide a conceptual framework for their evaluation.

In Drosophila, the functionally conserved transcription factor, doublesex (dsx), is pivotal to the specification of sexual identity in both males and females. One of its key dedicated roles involves regulating the development of a sexually dimorphic nervous system (NS) that underlies both male and female reproductive behaviours. Specific inhibition of the function of dsx-expressing neurons in males and females results in a global disruption of these sex-specific behavioural outputs. However, little is known about the functional organisation of this dsx circuit that encodes the potential to display these behaviours. Such investigations require the generation of a novel transgenic tool, capable of separating the function of dsx in the NS from that of the body. To achieve this, I generated a novel split-GAL4 dsx^GAL4-DBD hemidriver by ends in homologous recombination. Coupling the novel tool with the pan-neuronal elav^VP16-AD hemidriver, revealed spatial restriction of dsx^GAL4-DBD/elav^VP16-AD expression to dsx neurons only; enabling the realisation of novel patterns of dsx-expression in the peripheral NS. Next, the ability to elicit male-specific behavioural outputs upon activation of all dsx neurons formed the basis of a large behavioural screen aimed at parsing dsx circuitry into functionally distinct clusters. I utilised the novel dsx^GAL4-DBD hemidriver to screen a large collection of extant enhancer trap lines (ET^VP16-AD), for the elicitation of distinct sub-behaviours of male courtship. Here, I show that the activity of dsx-expressing clusters in: i) the brain (dsx-pC1, -pC2 and -pC3 collectively) regulate the early steps of male courtship (initiation, orientation and wing extension), ii) the pro- and mesothoracic ganglia (dsx-TN1 and -TN2) regulate the middle steps of male courtship (wing extension and possibly courtship song) and iii) the abdominal ganglia (dsx-Abg) regulate the late steps of male courtship (abdominal curling, attempted copulation and copulation). These data establish functional correlations between dsx clusters in distinct neuroanatomical foci and specific sub-behaviours of the courtship repertoire. Furthermore, the novel intersectional tool primed a collaborative study on female post-copulatory behaviours. We identified key sensory neurons in the female reproductive tract involved in initiating post-mating behaviours. Subsequent functional interrogations of dsx circuitry in the central NS revealed a subset of dsx-expressing neurons in the Abg that mediate changes in the female behavioural repertoire after mating. Characterisation of this relatively simple neural circuitry sheds light on the organisation of the fly brain. Ultimately, future studies will define principles of neural circuit operation, which may be similarly conserved in the nervous systems of higher animals.

No description available.

This dissertation research explored the capabilities of neuronal networks grown on substrate integrated microelectrode arrays in vitro with emphasis on utilizing such preparations in three specific application domains: pharmacology and drug development, biosensors and neurotoxicology, and the study of burst and synaptic mechanisms. Chapter 1 details the testing of seven novel AChE inhibitors, demonstrating that neuronal networks rapidly detect small molecular differences in closely related compounds, and reveal information about their probable physiological effects that are not attainable through biochemical characterization alone. Chapter 2 shows how neuronal networks may be used to classify and characterize an unknown compound. The compound, trimethylol propane phosphate (TMPP) elicited changes in network activity that resembled those induced by bicuculline, a known epileptogenic. Further work determined that TMPP produces its effects on network activity through a competitive inhibition of the GABAA receptor. This demonstrates that neuronal networks can provide rapid, reliable warning of the presence of toxic substances, and from the manner in which the spontaneous activity changes provide information on the class of compound present and its potential physiological effects. Additional simple pharmacological tests can provide valuable information on primary mechanisms involved in the altered neuronal network responses. Chapter 3 explores the effects produced by a radical simplification of synaptic driving forces. With all synaptic interactions pharmacologically limited to those mediated through the NMDA synapse, spinal cord networks exhibited an extremely regular burst oscillation characterized by a period of 2.9 ± 0.3 s, with mean coefficients of variation of 3.7, 4.7, and 4.9 % for burst rate, burst duration, and inter-burst interval, respectively (16 separate cultures). The reliability of expression of this oscillation suggests that it may represent a fundamental mechanism of importance during periods of NMDA receptor dominated activity, such as embryonic and early postnatal development. NMDA synapse mediated activity produces a precise oscillatory state that allows the study of excitatory-coupled network dynamics, burst mechanisms, emergent network properties, and structure-function relationships.

Spontaneously active monolayer neuronal networks cultured on photoetched multimicroelectrode plates (MMEPs) offer great potential for use in studying neuronal networks. However, there are many problems associated with frequent, long-term use of MMEPs. The major problems include (1) polysiloxane insulation deterioration and breakdown, (2) and loss of gold at the gold electroplated indium-tin oxide (ITO) electrodes. The objective of this investigation was to correct these major problems. Quality control measures were employed to monitor MMEP fabrication variables. The phenotypes of polysiloxane degradation were identified and classified. Factors that were found to contribute most to insulation deterioration were (1) moisture contamination during MMEP insulation, (2) loss of the quartz barrier layer from excessive exposure to basic solutions, and (3) repetitive use in culture. As a result, the insulation equipment and methods were modified to control moisture-dependent insulation deterioration, and the KOH reprocessing solution was replaced with tetramethylguanidine to prevent damage to the quartz. The problems associated with gold electroplating were solved via the addition of a pulsed-DC application of gold in a new citrate buffered electroplating solution.