Enacting a Mission for Change: A University Partnership for Young Adolescents

Abstract As practicing teachers, school personnel, and teacher educators engaged in a school-university partnership, we have worked to co-create a mutually beneficial relationship centered around the learning needs of young adolescents. In this article, we will describe our diverse perspectives on and perceptions of how the partnership enhances the learning experiences of the young adolescents with whom we learn and work. We come to this work with two interrelated goals of preparing a cadre of effective middle grades teachers while improving the educational experiences for 10-14-year-old students at Westport Middle School (WMS)--whether it is through classroom instruction, teacher education, or providing supports within the school

Asteroid Redirect Mission Proximity Operations for Reference Target Asteroid 2008 EV5

NASA's Asteroid Redirect Mission (ARM) is composed of two segments, the Asteroid Redirect Robotic Mission (ARRM), and the Asteroid Redirect Crewed Mission (ARCM). In March of 2015, NASA selected the Robotic Boulder Capture Option1 as the baseline for the ARRM. This option will capture a multi-ton boulder, (typically 2-4 meters in size) from the surface of a large (greater than approx.100 m diameter) Near-Earth Asteroid (NEA) and return it to cis-lunar space for subsequent human exploration during the ARCM. Further human and robotic missions to the asteroidal material would also be facilitated by its return to cis-lunar space. In addition, prior to departing the asteroid, the Asteroid Redirect Vehicle (ARV) will perform a demonstration of the Enhanced Gravity Tractor (EGT) planetary defense technique2. This paper will discuss the proximity operations which have been broken into three phases: Approach and Characterization, Boulder Capture, and Planetary Defense Demonstration. Each of these phases has been analyzed for the ARRM reference target, 2008 EV5, and a detailed baseline operations concept has been developed

Automated whole-cell patch-clamp electrophysiology of neurons in vivo

Whole-cell patch-clamp electrophysiology of neurons is a gold-standard technique for high-fidelity analysis of the biophysical mechanisms of neural computation and pathology, but it requires great skill to perform. We have developed a robot that automatically performs patch clamping in vivo, algorithmically detecting cells by analyzing the temporal sequence of electrode impedance changes. We demonstrate good yield, throughput and quality of automated intracellular recording in mouse cortex and hippocampus.National Institutes of Health (U.S.) (NIH EUREKA Award program (1R01NS075421))National Institutes of Health (U.S.) ((NIH) Director′s New Innovator Award (DP2OD002002)National Science Foundation (U.S.) ((NSF) CAREER award (CBET 1053233))New York Stem Cell Foundation (Robertson Neuroscience Award)Dr. Gerald Burnett and Marjorie BurnettNational Science Foundation (U.S.) (grant CISE 1110947)National Science Foundation (U.S.) (grant EHR 0965945)American Heart Association (10GRNT4430029

Gain in Stochastic Resonance: Precise Numerics versus Linear Response Theory beyond the Two-Mode Approximation

In the context of the phenomenon of Stochastic Resonance (SR) we study the correlation function, the signal-to-noise ratio (SNR) and the ratio of output over input SNR, i.e. the gain, which is associated to the nonlinear response of a bistable system driven by time-periodic forces and white Gaussian noise. These quantifiers for SR are evaluated using the techniques of Linear Response Theory (LRT) beyond the usually employed two-mode approximation scheme. We analytically demonstrate within such an extended LRT description that the gain can indeed not exceed unity. We implement an efficient algorithm, based on work by Greenside and Helfand (detailed in the Appendix), to integrate the driven Langevin equation over a wide range of parameter values. The predictions of LRT are carefully tested against the results obtained from numerical solutions of the corresponding Langevin equation over a wide range of parameter values. We further present an accurate procedure to evaluate the distinct contributions of the coherent and incoherent parts of the correlation function to the SNR and the gain. As a main result we show for subthreshold driving that both, the correlation function and the SNR can deviate substantially from the predictions of LRT and yet, the gain can be either larger or smaller than unity. In particular, we find that the gain can exceed unity in the strongly nonlinear regime which is characterized by weak noise and very slow multifrequency subthreshold input signals with a small duty cycle. This latter result is in agreement with recent analogue simulation results by Gingl et al. in Refs. [18, 19].Comment: 22 pages, 5 eps figures, submitted to PR

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Neural Decision Boundaries for Maximal Information Transmission

We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information transmitted about those signals. Our motivation comes from the nervous system, where neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.Comment: 5 pages, 3 figure

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures

The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks

We derive a simple unified framework giving closed-form estimates for the test risk and other generalization metrics of kernel ridge regression (KRR). Relative to prior work, our derivations are greatly simplified and our final expressions are more readily interpreted. These improvements are enabled by our identification of a sharp conservation law which limits the ability of KRR to learn any orthonormal basis of functions. Test risk and other objects of interest are expressed transparently in terms of our conserved quantity evaluated in the kernel eigenbasis. We use our improved framework to: i) provide a theoretical explanation for the "deep bootstrap" of Nakkiran et al (2020), ii) generalize a previous result regarding the hardness of the classic parity problem, iii) fashion a theoretical tool for the study of adversarial robustness, and iv) draw a tight analogy between KRR and a well-studied system in statistical physics

Strengthening the Springs: Improving Sprint Performance via Strength Training

How the inclusion of properly sequenced weightlifting derivatives into the strength-training program can improve sprint performance