506 research outputs found
Experimental Verification of Rate Flexibility and Probabilistic Shaping by 4D Signaling
The rate flexibility and probabilistic shaping gain of -dimensional
signaling is experimentally tested for short-reach, unrepeated transmission. A
rate granularity of 0.5 bits/QAM symbol is achieved with a distribution matcher
based on a simple look-up table.Comment: Presented at OFC'18, San Diego, CA, US
Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation
This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined.
The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies
Striatal Cholinergic Interneurons Are Required for Contending Strategy Selection While Solving Spatial Navigation Problems
How do animals adopt a given behavioral strategy to solve a recurrent problem when several effective strategies are available to reach the goal? Here we provide evidence that striatal cholinergic interneurons (SCINs) modulate their activity when mice must select between different strategies with similar goal-reaching effectiveness. Using a cell type-specific transgenic murine system, we show that adult SCIN ablation impairs strategy selection in navigational tasks where a goal can be independently achieved by adopting an allocentric or egocentric strategy. SCIN-depleted mice learn to achieve the goal in these tasks, regardless of their appetitive or aversive nature, in a similar way as controls. However, they cannot shift away from their initially adopted strategies, as control mice do, as training progresses. Our results indicate that SCINs are required for shaping the probability function used for strategy selection as experience accumulates throughout training. Thus, SCINs may be critical for the resolution of cognitive conflicts emerging when several strategies compete for behavioral control while adapting to environmental demands. Our findings may increase our understanding about the emergence of perseverative/compulsive traits in neuropsychiatric disorders with a reported SCIN reduction, such as Tourette and Williams syndromes.Fil: Beccaria, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Pretell Annan, Carlos Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Keifman, Ettel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Murer, Mario Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Belforte, Juan Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; Argentin
Setting the Occasion for Reward-seeking in Brain and Behavior
The ability to resolve uncertainty surrounding reward-associated cues is essential for the proper organization and generation of reward-seeking. Traditional approaches have modelled this dynamic process with distinct physical settings which preclude a collective neural and behavioral assessment of both how such contextual or higher-order cues are encoded and how they subsequently act on conditioned stimuli to in turn effect behavior. Here, I investigated the ability of rats to use higher-order cues to resolve the likelihood of reinforcement to an ambiguous conditioned stimulus, a process termed occasion setting. In Chapter 2, I characterized a novel approach to observing the hierarchical control of reward-seeking and discover unique motivational characteristics of higher-order contextual stimuli. In Chapter 3, I use optogenetic methods in combination with genetically modified rats to probe the contribution of dopamine neurons in the ventral tegmental area to occasion setting. These findings present a real-time contribution of the degree of dopamine neuron activity to the amount of reward-seeking which is consistent with reports of dopamine neurons encoding the expected utility of conditioned stimuli. In Chapter 4, I show that neural activity and dopamine signaling with the nucleus accumbens is essential for occasion setting. I further characterized the activity of nucleus accumbens neurons using in vivo electrophysiology and, in collaboration with colleagues at the University of Minnesota, monitored dopamine release in the nucleus accumbens to provide a mechanistic account of the role of the nucleus accumbens in the organization of reward-seeking. In Chapter 5, I find that basolateral amygdala and orbitofrontal cortex, but not dorsal hippocampus, are necessary for occasion setting. In Chapter 6, I described the encoding of occasion setters in the basolateral amygdala and the necessity of activity in the basolateral amygdala for this higher-order cue to influence conditioned reward-seeking. These results are contrasted with recordings and optogenetic manipulations in the orbitofrontal cortex. Collectively these results detail neural and behavioral mechanisms for the generation of flexible cue-triggered reward-seeking which have implications for our understanding of aberrant motivation in psychiatric illness
Exploring the Neural Mechanisms of Physics Learning
This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive
the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics
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