A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling.

We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.

Coupled Memristor Oscillators for Neuromorphic Locomotion Control: Modeling and Analysis.

The recent surge of interest in brain-inspired architectures along with the development of nonlinear dynamical electronic devices and circuits has enabled energy-efficient hardware realizations of several important neurobiological systems and features. Central pattern generator (CPG) is one such neural system underlying the control of various rhythmic motor behaviors in animals. A CPG can produce spontaneous coordinated rhythmic output signals without any feedback mechanism, ideally realizable by a system of coupled oscillators. Bio-inspired robotics aims to use this approach to control the limb movement for synchronized locomotion. Hence, devising a compact and energy-efficient hardware platform to implement neuromorphic CPGs would be of great benefit for bio-inspired robotics. In this work, we demonstrate that four capacitively coupled vanadium dioxide (VO 2 ) memristor-based oscillators can produce spatiotemporal patterns corresponding to the primary quadruped gaits. The phase relationships underlying the gait patterns are governed by four tunable bias voltages (or four coupling strengths) making the network programmable, reducing the complex problem of gait selection and dynamic interleg coordination to the choice of four control parameters. To this end, we first introduce a dynamical model for the VO 2 memristive nanodevice, then perform analytical and bifurcation analysis of a single oscillator, and finally demonstrate the dynamics of coupled oscillators through extensive numerical simulations. We also show that adopting the presented model for a VO 2 memristor reveals a striking resemblance between VO 2 memristor oscillators and conductance-based biological neuron models such as the Morris-Lecar (ML) model. This can inspire and guide further research on implementation of neuromorphic memristor circuits that emulate neurobiological phenomena.

Probing ultrafast excited-state dynamics using EUV-IR six-wave-mixing emission spectroscopy.

Non-linear processes such as four-wave-mixing have become instrumental in attosecond EUV spectroscopy. Using EUV high harmonics in conjunction with collinear near-infrared and mid-infrared fields, we extended this approach to high-order-mixing between three colors. Specifically, we find that atomic resonances in neon exhibit a significant cross section for six-wave mixing. The MIR probe frequency tunability in our multicolor scheme is employed to access several optically dark resonances and probe the quantum beat of a coherent electronic wavepacket using background-free EUV emission as a diagnostic. This technique can be easily extended to other atomic and molecular systems, and opens the door to multi-dimensional non-linear spectroscopy.

Modeling the tonotopic map using a two-dimensional array of neural oscillators.

We present a model of a tonotopic map known as the Oscillatory Tonotopic Self-Organizing Map (OTSOM). It is a 2-dimensional, self-organizing array of Hopf oscillators, capable of performing a Fourier-like decomposition of the input signal. While the rows in the map encode the input phase, the columns encode frequency. Although Hopf oscillators exhibit resonance to a sinusoidal signal when there is a frequency match, there is no obvious way to also achieve phase tuning. We propose a simple method by which a pair of Hopf oscillators, unilaterally coupled through a coupling scheme termed as modified power coupling, can exhibit tuning to the phase offset of sinusoidal forcing input. The training of OTSOM is performed in 2 stages: while the frequency tuning is adapted in Stage 1, phase tuning is adapted in Stage 2. Earlier tonotopic map models have modeled frequency as an abstract parameter unconnected to any oscillation. By contrast, in OTSOM, frequency tuning emerges as a natural outcome of an underlying resonant process. The OTSOM model can possibly be regarded as an approximation of the tonotopic map found in the primary auditory cortices of mammals, particularly exemplified in the studies of echolocating bats.

A Complex-Valued Oscillatory Neural Network for Storage and Retrieval of Multidimensional Aperiodic Signals.

Recurrent neural networks with associative memory properties are typically based on fixed-point dynamics, which is fundamentally distinct from the oscillatory dynamics of the brain. There have been proposals for oscillatory associative memories, but here too, in the majority of cases, only binary patterns are stored as oscillatory states in the network. Oscillatory neural network models typically operate at a single/common frequency. At multiple frequencies, even a pair of oscillators with real coupling exhibits rich dynamics of Arnold tongues, not easily harnessed to achieve reliable memory storage and retrieval. Since real brain dynamics comprises of a wide range of spectral components, there is a need for oscillatory neural network models that operate at multiple frequencies. We propose an oscillatory neural network that can model multiple time series simultaneously by performing a Fourier-like decomposition of the signals. We show that these enhanced properties of a network of Hopf oscillators become possible by operating in the complex-variable domain. In this model, the single neural oscillator is modeled as a Hopf oscillator, with adaptive frequency and dynamics described over the complex domain. We propose a novel form of coupling, dubbed "power coupling," between complex Hopf oscillators. With power coupling, expressed naturally only in the complex-variable domain, it is possible to achieve stable (normalized) phase relationships in a network of multifrequency oscillators. Network connections are trained either by Hebb-like learning or by delta rule, adapted to the complex domain. The network is capable of modeling N-channel electroencephalogram time series with high accuracy and shows the potential as an effective model of large-scale brain dynamics.

A Mixed-methods Study to Understand Food Environments and Grocery Shopping Patterns of Community Residents in Underserved Neighborhoods in Tampa, Florida.

A mixed methods study was conducted to better understand food access, food retail store environment, and perspectives of community residents on their grocery store shopping patterns and access to healthy foods in underserved, predominantly African American neighborhoods. GIS mapping, grocery store observations (n = 4), a food access and grocery store environment survey (n = 126), and focus groups (n = 48) were used. The results indicate that these neighborhoods have a low density of grocery stores, and only two out of four grocery stores meet the standard for a healthy retail store. Barriers to getting healthy foods and solutions to improve food access are discussed.

Does Separate Administration of Montelukast and Levocetirizine Provide Better Results in Perennial Allergic Rhinitis: A Hospital Based Study.

To determine whether separate administration of Montelukast and Levocetirizine provide a better response in perennial allergic rhinitis (PAR) than their fixed dose combination. Total 151 patients underwent a hospital based clinical study, being divided in 3 discrete groups. One group was given non-bilayered combination of Montelukast and Levocetrizine, 2nd group was given their bilayered counterpart whereas 3rd group was given the drugs at 12 h interval. Medications were continued for 3 months before stoppage. All patients were followed up for 1 month more to note recurrence of symptoms of PAR on weekly basis. While the combination formulation gave 9.8 and 12.6 % symptom-free patients; separate administration of the molecules gave 43.3 % positive (symptom-free 1 month) result. Fixed-dose combination is found to cause 1.4 times more chance of recurrence. Though per se, Montelukast and Levocetrizine does not give excellent response in PAR, their separate administration provides better outcome. So a morning dose of Montelukast and Levocetirizine at bed time is recommended while treating PAR.

The influence of ambient scent temperature on food consumption behavior.

This research examines the impact of ambient odor on food consumption. The results of a field experiment and 5 lab experiments show that the presence of a warm ambient odor (e.g., cedarwood) versus a cool ambient odor (e.g., eucalyptus) reduces the amount of calories consumed and also leads to increased choice of lower-calorie food options. This is attributable to established implicit associations formed from the human body's innate physiological response to changes in ambient temperature. Specifically, exposure to a warm (vs. cool) ambient odor influences perceived ambient temperature, which in turn alters food consumption behaviors. The results of this research extend the limited research examining the temperature dimension of odor and enhance the understanding of the role of sensory cues in influencing food consumption. Further, given the link between calorie consumption and widespread obesity worldwide, this research provides important implications for health and wellbeing. (PsycINFO Database Record (c) 2019 APA, all rights reserved).