The effects of cannabinoid agonists on spontaneous neuronal network activity were characterized in murine spinal cord and auditory cortical cultures with multichannel extracellular recording using photoetched electrode arrays. Different cultures responded reproducibly with global decreases of spiking and bursting to anandamide and methanandamide, but each agonist showed unique minor effects on network activity. The two tissues responded in a tissue-specific manner. Spontaneous activity in spinal tissue was terminated by 1 μM anandamide and 6.1 μM methanandamide. Cortical activity ceased at 3.5 μM and 2.8 μM respectively. Irreversible cessation of activity was observed beyond 8 μM for both tissues and test substances. Palmitoylethanolamide, demonstrated that CB2 receptors were not present or not responsive. However, the data strongly suggested the presence of CB1 receptors.

The hippocampus (HPC) is a brain area in the medial temporal lobe involved in spatial navigation, as well as the formation of episodic memories. A subset of the principal cells of the HPC, known as place cells, are active in specific locations of an environment, called the place fields. Dorsal hippocampal area CA1 contains place fields that are known to change their firing during spatial tasks where animals learn the location of a reward, known as goal-oriented learning (GOL) – CA1 place fields shift toward rewarded locations. Previous studies suggest that this preferentially occurs at novel rewarded locations in a familiar environment, but the mechanism is unknown. The locus coeruleus (LC) is a neuromodulatory nucleus in the brainstem that projects throughout the brain and releases norepinephrine and a small amount of dopamine. Stimulating locus coeruleus-hippocampal area CA1 projections (LC-CA1) was recently shown to improve performance on spatial memory tasks. Since performance on the GOL task is correlated with the degree of overrepresentation of rewarded locations, we hypothesized that the LC-CA1 projection was involved in reward-related place field reorganization. Using in vivo two photon calcium imaging, we recorded the activity of the LC-CA1 projection during a head fixed GOL task with two phases – during the first phase, a water reward was presented in one location (RZ1), and in the second phase, it was moved to a novel location (RZ2). In the first phase of the task, the LC-CA1 axons were correlated with running, but in the second phase they showed an increase in activity preceding RZ2. To determine whether the LC-CA1 is involved in place field reorganization that normally occurs in RZ2, we optogenetically activated the projection just before RZ1, and saw a pronounced place field reorganization right before the reward. Conversely, inhibition of LC-CA1 at RZ2 attenuated place field reorganization at this site. Finally, LC-CA1 stimulation away from the reward did not lead to place field reorganization, indicating that the LC influences place field shifts in conjunction with other signals that are differentially active around rewards. A full account of the effects of neuromodulation should also include astrocytes, since they respond to neuromodulators with large calcium signals that may be able to affect the function of neurons. We also recorded HPC astrocyte calcium activity during different behavioral tasks. Astrocytes showed occasional large calcium signals, with some differences in synchronicity and activity levels between hippocampal layers and behavioral paradigms. Future studies should determine whether the LC-CA1 projection affects place fields directly by affecting neural activity, indirectly via astrocytes, or both.

Modularity plays a pivotal role in evolution, as the compartmentalization of components of a system allows their independent optimization in isolation, minimizing the effect on the system as a whole. As a manifestation of this universal design principle, evidence suggests modularity plays a key role in the function of the brain as well, allowing the compartmentalization of specific structural and functional units before their integration. Despite this, it’s unknown how modularity arises during the development of neural circuits. Accordingly, observing the development of the modularity of the nervous system and correlating this with the emergence of specific behaviors has the potential to highlight features of the functional role of modularity in the mature nervous system. To explore this issue, we work with the small cnidarian Hydra vulgaris, a representative of some of the simplest nervous systems in evolution. Depending on the size of the animal, Hydra’s isometrically scaling nervous system of 300-2,000 neurons is organized in two independent nerve nets in its ectoderm and endoderm and is distributed through the body of the animal without any cephalization or ganglia. Moreover, under the right conditions Hydra can reassemble itself into a normal animal after complete dissociation into individual cells. Using transgenic Hydra which express the calcium sensor GCaMP6s in every neuron (Dupre and Yuste, 2017) we have imaged the neuronal activity of dissociated preparations as they re-aggregate then regenerate over a period of several days. We demonstrate the robust synchronization of Hydra’s neural nets during the process. Of the possible routes toward synchronization, we observe that an initially random structure takes on a hierarchical organization as small groups of neurons synchronize. As these proto-circuits further synchronize, the modularity of the system increases, accompanied by a loss of the hierarchical depth of the network structure as normal behavioral rhythms resume during regeneration.

An important question in sensory neuroscience is what coding strategies and mechanisms are used by the brain to detect and discriminate among behaviorally relevant stimuli. To address the noisy response properties of individual neurons, sensory systems often utilize broadly tuned neurons with overlapping receptive fields at the system's periphery, resulting in homogeneous responses among neighboring populations of neurons. It has been hypothesized that progressive response heterogeneity in ascending sensory pathways is evidence of an efficient encoding strategy that minimizes the redundancy of the peripheral neural code and maximizes information throughput for higher level processing. This hypothesis has been partly supported by the documentation of neural heterogeneity in various cortical structures. This dissertation examines whether selective and sensitive responses to behaviorally relevant stimuli contribute to a heterogeneous and efficient encoding in the auditory midbrain. Prior to this study, no compelling experimental framework existed to address this question. Stimulus design methodologies for neuroethological experiments were largely based on token vocalizations or simple approximations of vocalization components. This dissertation describes a novel state-space signal modeling methodology which makes possible the independent manipulation of the frequency, amplitude, duration, and harmonic structure of vocalization stimuli. This methodology was used to analyze four mouse vocalizations and create a suite of perturbed variants of each of these vocalizations. Responses of neurons in the mouse inferrior colliculus (IC) to the natural vocalizations and their perturbations were characterized using measures of both spike rate and spike timing. In order to compare these responses to those of peripheral auditory neurons, a data-driven model was developed and fit to each IC neuron based on the neuron's pure tone responses. These models were then used to approximate how peripheral auditory neurons would respond to our suite of vocalization stimuli. Using information theoretic measures, this dissertation argues that selectivity and sensitivity by individual neurons results in heterogeneous population responses in the IC and contributes to the efficient encoding of behaviorally relevant vocalizations.

The objective of the completed research was to characterize the morphology of individual neurons within monolayer networks of fetal mouse spinal tissue via intraperikaryal injections of horseradish peroxidase (HRP). Thirty labelled neurons were reconstructed via camera lucida drawings and morphometrically analyzed.

Thesis advisor: Gorica Petrovich / Thesis advisor: John Christianson / The influence of novelty on feeding behavior is significant and can override both homeostatic and hedonic drives due to the uncertainty of danger. The potential risks associated with consuming novel foods or consuming foods in a novel environment can lead to avoidance. While it is established that both novel foods and novel feeding environments can reduce or suppress feeding, it remains unclear how these two factors interact with each other to impact consumption and whether there are sex differences. Additionally, the neural mechanisms that underlie the impact of novelty on consumption are not well understood. This dissertation aimed to investigate the behavioral and neural mechanisms of the impact of novelty during food consumption in male and female rats. We first examined the consumption of novel and familiar foods in novel or familiar contexts for male and female rats (Chapter 2). Acutely food deprived rats were tested in either their familiar or novel context and were given two foods, one familiar and one novel. They underwent repeated consumption tests to allow us to track habituation to novelty overtime. Results indicated a robust behavioral sex difference in consumption during habituation. Males habituated to novel foods faster than females who showed suppressed consumption throughout testing. Next, we aimed to determine the neural circuitry mediating consumption of novel foods and feeding in novel environments (Chapter 3). Male and female rats were tested for consumption in either a familiar or in a novel context and were given either a familiar or novel food. Rats were perfused after testing to determine Fos induction. Results revealed increased activation in the novel context condition within several key areas: the central (CEA) and basolateral complex nuclei of the amygdala, the thalamic paraventricular (PVT) and reuniens nuclei, the nucleus accumbens (ACB), and the medial prefrontal cortex prelimbic and infralimbic areas. Additionally, novel food condition increased activation within the CEA, anterior basomedial nucleus of the amygdala, and anterior PVT. Sex differences in activation patterns were also observed within specific regions. The capsular and lateral CEA had greater activation for male groups and the anterior PVT, ACBv core, and ACB ventral shell had greater activation for female groups. We also investigated different patterns of related regions and the nature of those relationships and found that the CEA is a pivotal hub in our network. Therefore, we investigated the recruitment of specific inputs to the CEA in male and female rats during consumption of a novel food in a novel context (Chapter 4). We used a combination of retrograde tract tracing and Fos induction to determine whether PVTp, ILA, and AId neurons that send direct projections to the CEA were specifically recruited during the consumption test under novelty and whether that activation was sex specific. Results indicated that during consumption of a novel food in a novel context, connections from the PVTp to the CEA were recruited more heavily compared to rats that were consuming familiar food in a familiar context. These results suggest that projections from the PVTp to CEA may be driving the inhibition of feeding during novelty processing. Overall, this dissertation provides valuable insights into the behavioral and neural mechanisms of consumption under novelty and allows us to begin building the circuitry that underlies feeding inhibition during novelty processing. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.

Despite considerable progress in mapping the fly’s connectome and elucidating the patterns of information flow in its brain, the complexity of the fly brain’s structure and the still-incomplete state of knowledge regarding its neural circuitry pose significant challenges beyond satisfying the computational resource requirements of current fly brain models that must be addressed to successfully reverse the information processing capabilities of the fly brain. These include the need to explicitly facilitate collaborative development of brain models by combining the efforts of multiple researchers, and the need to enable programmatic generation of brain models that effectively utilize the burgeoning amount of increasingly detailed publicly available fly connectome data. This thesis presents an open pipeline for modular construction of executable models of the fruit fly brain from incomplete biological brain data that addresses both of the above requirements. This pipeline consists of two major open-source components respectively called Neurokernel and NeuroArch. Neurokernel is a framework for collaborative construction of executable connectome-based fly brain models by integration of independently developed models of different functional units in the brain into a single emulation that can be executed upon multiple Graphics Processing Units (GPUs). Neurokernel enforces a programming model that enables functional unit models that comply with its interface requirements to communicate during execution regardless of their internal design. We demonstrate the power of this programming model by using it to integrate independently developed models of the fly retina and lamina into a single vision processing system. We also show how Neurokernel’s communication performance can scale over multiple GPUs, number of functional units in a brain emulation, and over the number of communication ports exposed by a functional unit model. Although the increasing amount of experimentally obtained biological data regarding the fruit fly brain affords brain modelers a potentially valuable resource for model development, the actual use of this data to construct executable neural circuit models is currently challenging because of the disparate nature of different data sources, the range of storage formats they use, and the limited query features of those formats complicates the process of inferring executable circuit designs from biological data. To overcome these limitations, we created a software package called NeuroArch that defines a data model for concurrent representation of both biological data and model structure and the relationships between them within a single graph database. Coupled with a powerful interface for querying both types of data within the database in a uniform high-level manner, this representation enables construction and dispatching of executable neural circuits to Neurokernel for execution and evaluation. We demonstrate the utility of the NeuroArch/Neurokernel pipeline by using the packages to generate an executable model of the central complex of the fruit fly brain from both published and hypothetical data regarding overlapping neuron arborizations in different regions of the central complex neuropils. We also show how the pipeline empowers circuit model designers to devise computational analogues to biological experiments such as parallel concurrent recording from multiple neurons and emulation of genetic mutations that alter the fly’s neural circuitry.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

The human brain consists of billions of neurons and these neurons pool together in groups at different scales. On one hand, these neural entities tend to behave as single units and on the other hand show collective macroscopic patterns of activity. The neural units communicate with each other and process information over time. This communication is through small electrical impulses which at the macroscopic scale are measurable as brain waves. The electric field that is produced collectively by macroscopic groups of neurons within the brain can be measured on the surface of the skull via a brain imaging modality called Electroencephalography (EEG). The brain as a neural system has variant connection topology, in which an area might not only be connected to its adjacent neighbors homogeneously but also distant areas can directly transfer brain activity [16]. Timing of these brain activity communications between different neural units bring up overall emerging spatiotemporal patterns. The dynamics of these patterns and formation of neural activities in cortical surface is influenced by the presence of long-range connections between heterogeneous neural units. Brain activity at large-scale is thought to be involved in the information processing and the implementation of cognitive functions of the brain. This research aims to determine how the spatiotemporal pattern formation phenomena in the brain depend on its connection topology. This connection topology consists of homogeneous connections in local cortical areas alongside the couplings between distant functional units as heterogeneous connections. Homogeneous connectivity or synaptic weight distribution representing the large-scale anatomy of cortex is assumed to depend on the Euclidean distance between interacting neural units. Altering characteristics of inhomogeneous pathways as control parameters guide the brain pattern formation through phase transitions at critical points. In this research, linear stability analysis is applied to a macroscopic neural field in a one-dimensional circular and a twodimensional spherical model of the brain in order to find destabilization mechanism and subsequently emerging patterns. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

by Lee Tan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [128]-[130] / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Introduction to Artificial Neural Networks --- p.1-4 / Chapter 1.1.1 --- Fundamentals of Artificial Neural Networks --- p.1-5 / Chapter 1.1.2 --- Various Artificial Neural Network Models ´ؤA Review --- p.1-11 / Chapter 1.2 --- Introduction to Fuzzy Sets Theory --- p.1-17 / Chapter 1.2.1 --- "Fuzziness, Fuzzy sets and Membership Function" --- p.1-17 / Chapter 1.2.2 --- Applications of Fuzzy Sets --- p.1-19 / Connective Summary --- p.1-21 / Chapter CHAPTER 2 --- LEARNING WITH FUZZY INFORMATION --- p.2-1 / Chapter 2.1 --- "Decision Making, Pattern Associating and Pattern Classification" --- p.2-3 / Chapter 2.2 --- Artificial Neural Networks as Learning Decision Systems --- p.2-6 / Chapter 2.3 --- Fuzziness in Decision Making Processes --- p.2-10 / Chapter 2.4 --- Learning with Fuzzy Information --- p.2-12 / Chapter 2.5 --- The Formulation of Our Approach --- p.2-16 / Connective Summary --- p.2-18 / Chapter CHAPTER 3 --- A MODIFIED BACKPROPAGATION ALGORITHM FOR MULTILAYER FEEDFORWARD NETWORKS --- p.3-1 / Chapter 3.1 --- Preliminaries --- p.3-3 / Chapter 3.2 --- The Error Backpropagation Algorithm (EBPA) --- p.3-8 / Chapter 3.3 --- A Modified EBPA Learning with A Priori Fuzzy Information --- p.3-11 / Chapter 3.3.1 --- The Membership-Weighed Objective Function --- p.3-11 / Chapter 3.3.2 --- The Fuzzy Error Backpropagation Algorithm --- p.3-13 / Chapter 3.4 --- Discussion on the Proposed Fuzzy EBPA --- p.3-15 / Chapter 3.4.1 --- Methods of Determining Membership Functions --- p.3-15 / Chapter 3.4.2 --- Fuzzy EBPA Alters the Effective Target Patterns --- p.3-19 / Chapter 3.4.3 --- Estimating the Learning Rates Required for the Fuzzy EBPA --- p.3-21 / Connective Summary --- p.3-24 / Chapter CHAPTER 4 --- APPLICATION EXAMPLES --- p.4-1 / Chapter 4.1 --- A Single Node Classifier --- p.4-2 / Chapter 4.2 --- The Fuzzy XOR Problem --- p.4-29 / Chapter 4.2.1 --- Network Configuration 1 --- p.4-36 / Chapter 4.2.2 --- Network Configuration 2 --- p.4-46 / Chapter 4.2.3 --- Comments on the Simulation Results --- p.4-50 / Chapter 4.3 --- A Speech Recognition System --- p.4-54 / Connective Summary --- p.4-59 / Chapter CHAPTER 5 --- DISCUSSION AND CONCLUSION --- p.5-1