Is it time to forget science? Reflections on singular science and its history

The name history of science refl ects a set of assumptions about what science is. Among them is the claim that science is a singular thing, a potentially unifi ed group of disciplines that share a common identity. Long promoted by scientists and philosophers on the basis of a supposedly universal scientifi c method, this claim now looks very embattled. I trace its development from the early nineteenth century and the growth of the positivist movement to its various manifestations in the twentieth century. Recently, some historians have called for the term science to be relinquished, and for adoption of a more relaxed pluralism. Yet the complex legacy of the notion of singular science cannot be so easily abandoned

Review of: Robert Mitchell, Experimental Life: Vitalism in Romantic Science and Literature

Review by Jan Golinski (University of New Hampshire) of Robert Mitchell, Experimental Life: Vitalism in Romantic Science and Literature. Baltimore: Johns Hopkins University Press, 2013. Pp. 309. ISBN 9781421410883

Adaptive Gaussian inverse regression with partially unknown operator

This work deals with the ill-posed inverse problem of reconstructing a function $f$ given implicitly as the solution of $g = Af$, where $A$ is a compact linear operator with unknown singular values and known eigenfunctions. We observe the function $g$ and the singular values of the operator subject to Gaussian white noise with respective noise levels $\varepsilon$ and $\sigma$. We develop a minimax theory in terms of both noise levels and propose an orthogonal series estimator attaining the minimax rates. This estimator requires the optimal choice of a dimension parameter depending on certain characteristics of $f$ and $A$. This work addresses the fully data-driven choice of the dimension parameter combining model selection with Lepski's method. We show that the fully data-driven estimator preserves minimax optimality over a wide range of classes for $f$ and $A$ and noise levels $\varepsilon$ and $\sigma$. The results are illustrated considering Sobolev spaces and mildly and severely ill-posed inverse problems

A universal model for spike-frequency adaptation

Spike-frequency adaptation is a prominent feature of neural dynamics. Among other mechanisms, various ionic currents modulating spike generation cause this type of neural adaptation. Prominent examples are voltage-gated potassium currents (M-type currents), the interplay of calcium currents and intracellular calcium dynamics with calcium-gated potassium channels (AHP-type currents), and the slow recovery from inactivation of the fast sodium current. While recent modeling studies have focused on the effects of specific adaptation currents, we derive a universal model for the firing-frequency dynamics of an adapting neuron that is independent of the specific adaptation process and spike generator. The model is completely defined by the neuron's onset f-I curve, the steady-state f-I curve, and the time constant of adaptation. For a specific neuron, these parameters can be easily determined from electrophysiological measurements without any pharmacological manipulations. At the same time, the simplicity of the model allows one to analyze mathematically how adaptation influences signal processing on the single-neuron level. In particular, we elucidate the specific nature of high-pass filter properties caused by spike-frequency adaptation. The model is limited to firing frequencies higher than the reciprocal adaptation time constant and to moderate fluctuations of the adaptation and the input current. As an extension of the model, we introduce a framework for combining an arbitrary spike generator with a generalized adaptation current

Burst of the 1969 Leonids and 1982 Lyrids

Radar observations of the last bursts of the Leonids in 1969 and Lyrids in 1982, carried out at the Springhill Meteor Observatory, Canada, both of very short duration, with the rates exceeding a quarter-maximum rate within 50-55 minutes, are used for a study of the mass distribution of meteoroids. In both cases the mass distribution exponents of the meteoroids in the dense clouds largely differ from the values obtained for the older populations of the streams. The highest mass exponent s approximately 2.2-2.4 is found around the peak of the activity, confirming high contribution of smaller meteoroids, and thus also a recent origin of the dense clouds. Consequences of these findings are discussed