[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models

Kalman filters provide a straightforward and interpretable means to estimate hidden or latent variables, and have found numerous applications in control, robotics, signal processing, and machine learning. One such application is neural decoding for neuroprostheses. In 2020, Burkhart et al. thoroughly evaluated their new version of the Kalman filter that leverages Bayes' theorem to improve filter performance for highly non-linear or non-Gaussian observation models. This work provides an open-source Python alternative to the authors' MATLAB algorithm. Specifically, we reproduce their most salient results for neuroscientific contexts and further examine the efficacy of their filter using multiple random seeds and previously unused trials from the authors' dataset. All experiments were performed offline on a single computer

Export and economic growth in Africa

Call number: LD2668 .T4 1978 K45Master of Art

Uncovering temporal structure in hippocampal output patterns.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory

Enhanced optical properties of yttrium aluminum garnet with the yttrium vanadate impurity phase

Yttrium aluminum garnet doped with europium with an additional impurity phase of yttrium vanadate doped europium has been prepared in different ways: synthesized by a sol-gel route and mechanically mixed in a mortar. The obtained samples were characterized by X-ray diffraction analysis, and scanning electron microscopy. Photoluminescence spectra were recorded to understand the role of the impurity phase in the garnet's optical properties. The impurity phase showed a significant contribution to the optical properties of Y3Al5O12:1%Eu. --//-- Monika Skruodiene, Ruta Juodvalkyte, Meldra Kemere, Rimantas Ramanauskas, Anatolijs Sarakovskis, Ramunas Skaudzius, Enhanced optical properties of yttrium aluminum garnet with the yttrium vanadate impurity phase, Heliyon, Volume 8, Issue 11, 2022, e11386, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2022.e11386. (https://www.sciencedirect.com/science/article/pii/S2405844022026743). Published under the CC BY-NC-ND licence.ERDF [1.1.1.2/VIAA/3/19/480]; the Institute of Solid State Physics, University of Latvia (Latvia), as the Centre of Excellence has received funding from the European Union’s Horizon 2020 Frame-work Programme H2020-WIDESPREAD-01-2016-2017-Teaming Phase2 under grant agreement No. 739508, project CAMART

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

Developing Next-generation Brain Sensing Technologies - A Review.

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients

Hydrothermal Synthesis and Properties of Yb3+/Tm3+ Doped Sr2LaF7 Upconversion Nanoparticles

We report the procedure for hydrothermal synthesis of ultrasmall Yb3+/Tm3+ co-doped Sr2LaF7 (SLF) upconversion phosphors. These phosphors were synthesized by varying the concentrations of Yb3+ (x = 10, 15, 20, and 25 mol%) and Tm3+ (y = 0.75, 1, 2, and 3 mol%) with the aim to analyze their emissions in the near IR spectral range. According to the detailed structural analysis, Yb3+ and Tm3+ occupy the La3+ sites in the SLF host. The addition of Yb3+/Tm3+ ions has a huge impact on the lattice constant, particle size, and PL emission properties of the synthesized SLF nanophosphor. The results show that the optimal dopant concentrations for upconversion luminescence of Yb3+/Tm3+ co-doped SLF are 20 mol% Yb3+ and 1 mol% Tm3+ with EDTA as the chelating agent. Under 980 nm light excitation, a strong upconversion emission of Tm3+ ions around 800 nm was achieved. In addition, the experimental photoluminescence lifetime of Tm3+ emission in the SLF host is reported. This study discovered that efficient near IR emission from ultrasmall Yb3+/Tm3+ co-doped SLF phosphors may have potential applications in the fields of fluorescent labels in bioimaging and security applications

Phase separation and crystallization of La2O3 doped ZnO-B2O3-SiO2 glass

In order to investigate the effect of the La2O3 on the phase separation and crystallization of ZnO-B2O3-SiO2 glass, after the occurence of the phase separation and crystallization of glasses by heat treatment, the microstructure morphology and distribution of elements in different sample areas were characterized by the scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS); the non-isothermal crystallization kinetics of the glass samples was studied by using a differential scanning calorimeter (DSC) and the precipitated crystals of crystallized glass were determined by the X-ray diffraction (XRD). The results suggest that the phase separation and crystallization of 60ZnO-30B2O3-10SiO2 glass occur at glass surface, and the incorporation of small amount (<4 mol%) of La2O3 significantly inhibits the glass phase separation and consequently improves the thermal stability of glass. Doping of La2O3 accelerates the glass crystallization at the elevated temperature (660 °C), making the depth of crystal layer thicker and diffraction intensity in XRD patterns stronger. However, due to the precipitation of several crystals that occur simultaneously when La2O3 doping amount is 4 mol%, crystallization of the 60ZnO-30B2O3-10SiO2 glass is obviously depressed, the crystallization activation energy Ec and the relative crystallinity Xc of the glass reach the maximum and the minimum values, respectively. Although transition from one-dimensional growth of crystals to two-dimensional growth of crystals results from La2O3 addition, the one-dimensional growth of crystals remains dominant in crystallization process. This work can provide some useful information for preparing glass ceramics with nano-crystals precipitated in the glass surface

Measuring Instantaneous Frequency of Local Field Potential Oscillations using the Kalman Smoother

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100–250 Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.National Institutes of Health (U.S.) (NIH/NIMH R01 MH59733)National Institutes of Health (U.S.) (NIH/NIHLB R01 HL084502)Massachusetts Institute of Technology (Henry E. Singleton Presidential Graduate Fellowship Award