Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe

Recordings from neurons in the insects' olfactory primary processing center, the antennal lobe (AL), reveal that the AL is able to process the input from chemical receptors into distinct neural activity patterns, called olfactory neural codes. These exciting results show the importance of neural codes and their relation to perception. The next challenge is to \emph{model the dynamics} of neural codes. In our study, we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a neural network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons, and is capable of producing unique olfactory neural codes for the tested odorants. Specifically, we (i) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii) characterize scent recognition, i.e., decision-making based on olfactory signals and (iii) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study answers a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns

Nonlinear model order reduction via dynamic mode decomposition

We propose a new technique for obtaining reduced order models for nonlinear dynamical systems. Specically, we advocate the use of the recently developed dynamic mode decomposition (DMD), an equation-free method, to approximate the nonlinear term. DMD is a spatio-temporal matrix decomposition of a data matrix that correlates spatial features while simul-taneously associating the activity with periodic temporal behavior. With this decomposition, one can obtain a fully reduced dimensional surrogate model and avoid the evaluation of the nonlinear term in the online stage. This allows for a reduction in the computational cost and, at the same time, accurate approximations of the problem. We present a suite of numerical tests to illustrate our approach and to show the e ectiveness of the method in comparison to existing approaches

Randomized model order reduction

The singular value decomposition (SVD) has a crucial role in model order reduction. It is often utilized in the offline stage to compute basis functions that project the high-dimensional nonlinear problem into a low-dimensional model which is then evaluated cheaply. It constitutes a building block for many techniques such as the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). The aim of this work is to provide an efficient computation of low-rank POD and/or DMD modes via randomized matrix decompositions. This is possible due to the randomized singular value decomposition (rSVD) which is a fast and accurate alternative of the SVD. Although this is considered an offline stage, this computation may be extremely expensive; therefore, the use of compressed techniques drastically reduce its cost. Numerical examples show the effectiveness of the method for both POD and DMD

Target-Normal Single Spin Asymmetries Measured with Positrons

Two-photon exchange and the larger class of hadronic box diagrams are difficult to calculate without a large degree of model-dependence. At the same time, these processes are significant radiative corrections in parity-violating electron scattering, in neutron decay, and may even be responsible for the proton's form factor ratio discrepancy. New kinds of experimental data are needed to help constrain models and guide future box-diagram calculations. The target-normal single spin asymmetry, $A_n$, formed with an unpolarized beam scattering from a target that is polarized normal to the scattering plane, is sensitive to the imaginary part of the two-photon exchange amplitude, and can provide a valuable constraint. A measurement with both electrons and positrons can reduce sources of experimental error, and distinguish between the effects of two-photon exchange and those of time-reversal symmetry violation. This article describes a proposed experiment in Hall A, using the new Super Big-Bite Spectrometer that can cover a momentum transfer range in the critical zone of uncertainty between where hadronic calculations and those based on partonic degrees of freedom are expected to be accurate.Comment: 7 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2007.1508

Psychological Capital and Professional Identity: A Study of Professional Business Students

This research article reports the results and findings of an online survey questionnaire administered to 593 Masters of Business Administration (MBA) students using the MCPIS-9 and PCQ-12 instruments that measure Professional Identity (ProfId) and Psychological Capital (PsyCap), respectively. The results indicated a strong sense of ProfId (M = 4.2/5.0, SD = 0.66, N = 593), and a significant and positive relationship (p < .01, ΔR2adj = .25, N = 593) between PsyCap and ProfId. The results of this study represent a fruitful, albeit initial, foray into the ProfId and PsyCap of professional business students. The implications of these results inform and equip program stakeholders to devise curricular and pedagogical approaches to support students’ sense of self in their career trajectory