Inferring decoding strategy from choice probabilities in the presence of noise correlations

The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship often quantified by choice probabilities. While choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. Here, we derive the mathematical relationship between choice probabilities, read-out weights and noise correlations within the standard neural decision making model. Our solution allows us to prove and generalize earlier observations based on numerical simulations, and to derive novel predictions. Importantly, we show how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we present a test to decide whether the decoding weights of individual neurons are optimal, even without knowing the underlying noise correlations. We confirm the practical feasibility of our approach using simulated data from a realistic population model. Our work thus provides the theoretical foundation for a growing body of experimental results on choice probabilities and correlations

Signatures of criticality arise in simple neural population models with correlations

Large-scale recordings of neuronal activity make it possible to gain insights into the collective activity of neural ensembles. It has been hypothesized that neural populations might be optimized to operate at a 'thermodynamic critical point', and that this property has implications for information processing. Support for this notion has come from a series of studies which identified statistical signatures of criticality in the ensemble activity of retinal ganglion cells. What are the underlying mechanisms that give rise to these observations? Here we show that signatures of criticality arise even in simple feed-forward models of retinal population activity. In particular, they occur whenever neural population data exhibits correlations, and is randomly sub-sampled during data analysis. These results show that signatures of criticality are not necessarily indicative of an optimized coding strategy, and challenge the utility of analysis approaches based on equilibrium thermodynamics for understanding partially observed biological systems.Comment: 36 pages, LaTeX; added journal reference on page 1, added link to code repositor

Bayesian Inference for Generalized Linear Models for Spiking Neurons

Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate

Лазерное инициирование порошков тэна в условиях объемного сжатия

Определены энергетические пороги инициирования и исследована кинетика процесса взрывного разложения порошков тетранитрата пентаэритрита, объемносжатых до давления 5·108Н/м2, при воздействии импульсом лазерного излучения на длинах волн 1064 нм (область прозрачности) и 266 нм (область собственного поглощения). Реализованы условия низкопорогового инициирования для порошков чистого тэна первой, второй и четвертой гармониках излучения неодимового лазера

Influence of damping on the excitation of the double giant resonance

We study the effect of the spreading widths on the excitation probabilities of the double giant dipole resonance. We solve the coupled-channels equations for the excitation of the giant dipole resonance and the double giant dipole resonance. Taking Pb+Pb collisions as example, we study the resulting effect on the excitation amplitudes, and cross sections as a function of the width of the states and of the bombarding energy.Comment: 8 pages, 3 figures, corrected typo

A multi-detector array for high energy nuclear e+e- pair spectrosocopy

A multi-detector array has been constructed for the simultaneous measurement of energy- and angular correlation of electron-positron pairs produced in internal pair conversion (IPC) of nuclear transitions up to 18 MeV. The response functions of the individual detectors have been measured with mono-energetic beams of electrons. Experimental results obtained with 1.6 MeV protons on targets containing $^{11}$B and $^{19}$F show clear IPC over a wide angular range. A comparison with GEANT simulations demonstrates that angular correlations of $e^+e^-$ pairs of transitions in the energy range between 6 and 18 MeV can be determined with sufficient resolution and efficiency to search for deviations from IPC due to the creation and subsequent decay into $e^+e^-$ of a hypothetical short-lived neutral boson.Comment: 20 pages, 8 figure