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

Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

Simultaneously recorded neurons exhibit correlations whose underlying causes are not known. Here, we use a population of threshold neurons receiving correlated inputs to model neural population recordings. We show analytically that small changes in second-order correlations can lead to large changes in higher-order redundancies, and that the resulting interactions have a strong impact on the entropy, sparsity, and statistical heat capacity of the population. Our findings for this simple model may explain some surprising effects recently observed in neural population recordings

Bayesian estimation of orientation preference maps

Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial and temporal scales. Here, we present Bayesian methods based on Gaussian processes for extracting topographic maps from functional imaging data. In particular, we focus on the estimation of orientation preference maps (OPMs) from intrinsic signal imaging data. We model the underlying map as a bivariate Gaussian process, with a prior covariance function that reflects known properties of OPMs, and a noise covariance adjusted to the data. The posterior mean can be interpreted as an optimally smoothed estimate of the map, and can be used for model based interpolations of the map from sparse measurements. By sampling from the posterior distribution, we can get error bars on statistical properties such as preferred orientations, pinwheel locations or pinwheel counts. Finally, the use of an explicit probabilistic model facilitates interpretation of parameters and quantitative model comparisons. We demonstrate our model both on simulated data and on intrinsic signaling data from ferret visual cortex

Transport Coefficients from Large Deviation Functions

We describe a method for computing transport coefficients from the direct evaluation of large deviation function. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which is a scaled cumulant generating function analogous to the free energy. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green-Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.Comment: 11 pages, 5 figure

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

DNA-cellulose: an economical, fully recyclable and highly effective chiral biomaterial for asymmetric catalysis

similarity_check: This document is Similarity Check deposited related_data: Supplementary Information copyright_licence: The Royal Society of Chemistry has an exclusive publication licence for this journal peer_review_method: Single-blind history: Received 20 December 2014; Accepted 11 January 2015; Accepted Manuscript published 14 January 2015; Advance Article published 23 January 2015; Version of Record published 24 March 2015This research was supported by the Ministe`re de l’Enseignement Supe´rieur et de la Recherche and the Agence Nationale de la Recherche (NCiS; ANR-2010-JCJC-715-1)

Multilayer neural networks with extensively many hidden units

The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter the storage capacity if found to scale with the logarithm of the number of implementable Boolean functions. The generalization behaviour is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones.Comment: 4 pages, 2 figure