Point process modeling and estimation: advances in the analysis of dynamic neural spiking data

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments

Decoding the Game: A Quantitative Analysis of Market Manipulation and Machine-Human Interactions

University of Technology Sydney. Faculty of Business.The financial markets have experienced a technology revolution, resulting in innovations such as the advent of limit order books, increased social media use in investment decisions, and expanded FinTech services. This dissertation investigates market manipulation and machine-human interactions within this modern finance landscape. Chapter 2 introduces Q-learning, a novel machine learning technique, to explore its impact on strategic trading behavior in a dynamic limit order market. In equilibrium, informed traders learn to manipulate the market by using market buys (resp. sells) to trigger uninformed market buys (resp. sells) to enhance profitability of later informed limit sells (resp. buys). Chapter 3 analyses meme investing, a retail buying frenzy coordinated through social media, and its effect on investment efficiency. Modeling the frenzy as short-sale frictions reveals that small costs on short sellers can improve investment efficiency, while higher costs or bans harm it. Chapter 4 investigates the impact of growth in quantitative investing on price efficiency. Quants tend to trade more (resp. less) aggressively due to greater information processing (resp. weaker flexibility to adapt to market conditions) than discretionary traders. Consequently, the price efficiency is non-monotonic with respect to the level of quant trading

Topological Single Photon Emission from Quantum Emitter Chains

We develop a scheme of generating highly indistinguishable single photons from an active quantum Su-Schrieffer-Heeger chain made from a collection of noisy quantum emitters. Surprisingly, the single photon emission spectrum of the active quantum chain is extremely narrow compared to that of a single emitter or topologically trivial chain. Moreover, this effect becomes dramatically strong close to the non-trivial-to-trivial phase transition point. Using this effect, we show that the single photon linewidth of a long topological quantum chain can become arbitrarily narrow, constituting an ideal source of indistinguishable single photons. Finally, taking specific examples of actual quantum emitters, we provide a microscopic and quantitative analysis of our model and analyze the most important parameters in view of the experimental realization

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision, such as real-time decoding for brain-computer interfaces. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes's rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat's position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalent to or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain

Effect of Two Kinds of Bone Replacement Materials on Bone Formation in Repairing Bone Defects Around Mandibular Posterior Area: a Case Study of Bone Defects Around Mandibular Posterior Area Caused by Boxing

Objective: To investigate the effect of two kinds of bone replacement materials on bone formation in repairing bone defect around mandibular posterior area. Methods: A total of 60 patients with Bone defects around mandibular posterior area caused by boxing were selected from a hospital from January 2020 to June 2020. They were divided into Perio Glas (group P) and Bone Plant (group B) by random number table method, with 30 patients in each group. Perio Glas Bone graft was used in group P and Bone Plant graft was used in group B. The vertical height and buccal lingual bone plate width of the two groups were observed at baseline and after treatment, and the success rate of implants was compared between the two groups. Results: The success rate of implant in group P was significantly lower than that in group B (P < 0.05). The vertical height and buccal lingual bone plate width in group P were significantly lower than those in group B (P < 0.05). Conclusion: Compared with Perio Glas, Bone Plant can better maintain the vertical height and buccal lingual Bone plate width of patients with Bone defects around mandibular posterior area caused by boxing, and has better effect of inducing Bone regeneration and osteogenesis

DNA modification by sulfur: analysis of the sequence recognition specificity surrounding the modification sites

The Dnd (DNA degradation) phenotype, reflecting a novel DNA modification by sulfur in Streptomyces lividans 1326, was strongly aggravated when one (dndB) of the five genes (dndABCDE) controlling it was mutated. Electrophoretic banding patterns of a plasmid (pHZ209), reflecting DNA degradation, displayed a clear change from a preferential modification site in strain 1326 to more random modifications in the mutant. Fourteen randomly modifiable sites on pHZ209 were localized, and each seemed to be able to be modified only once. Residues in a region (5′-c–cGGCCgccg-3′) including a highly conserved 4-bp central core (5′-GGCC-3′) in a well-documented preferential modification site were assessed for their necessity by site-directed mutagenesis. While the central core (GGCC) was found to be stringently required in 1326 and in the mutant, ‘gccg’ flanking its right could either abolish or reduce the modification frequency only in the mutant, and two separate nucleotides to the left had no dramatic effect. The lack of essentiality of DndB for S-modification suggests that it might only be required for enhancing or stabilizing the activity of a protein complex at the required preferential modification site, or resolving secondary structures flanking the modifiable site(s), known to constitute an obstacle for efficient modification