Using spike train distances to identify the most discriminative neuronal subpopulation

Background: Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses. New Method: For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best. Results: The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease. Comparison with Existing Methods: Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary. Conclusions: The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.Comment: 14 pages, 9 Figure

Surrogate time series

Before we apply nonlinear techniques, for example those inspired by chaos theory, to dynamical phenomena occurring in nature, it is necessary to first ask if the use of such advanced techniques is justified "by the data". While many processes in nature seem very unlikely a priori to be linear, the possible nonlinear nature might not be evident in specific aspects of their dynamics. The method of surrogate data has become a very popular tool to address such a question. However, while it was meant to provide a statistically rigorous, foolproof framework, some limitations and caveats have shown up in its practical use. In this paper, recent efforts to understand the caveats, avoid the pitfalls, and to overcome some of the limitations, are reviewed and augmented by new material. In particular, we will discuss specific as well as more general approaches to constrained randomisation, providing a full range of examples. New algorithms will be introduced for unevenly sampled and multivariate data and for surrogate spike trains. The main limitation, which lies in the interpretability of the test results, will be illustrated through instructive case studies. We will also discuss some implementational aspects of the realisation of these methods in the TISEAN (http://www.mpipks-dresden.mpg.de/~tisean) software package.Comment: 28 pages, 23 figures, software at http://www.mpipks-dresden.mpg.de/~tisea

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

SPI/INTEGRAL in-flight performance

The SPI instrument has been launched on-board the INTEGRAL observatory on October 17, 2002. SPI is a spectrometer devoted to the sky observation in the 20 keV-8 MeV energy range using 19 germanium detectors. The performance of the cryogenic system is nominal and allows to cool the 19 kg of germanium down to 85 K with a comfortable margin. The energy resolution of the whole camera is 2.5 keV at 1.1 MeV. This resolution degrades with time due to particle irradiation in space. We show that the annealing process allows the recovery of the initial performance. The anticoincidence shield works as expected, with a low threshold at 75 keV, reducing the GeD background by a factor of 20. The digital front-end electronics system allows the perfect alignement in time of all the signals as well as the optimisation of the dead time (12%). We demonstrate that SPI is able to map regions as complex as the galactic plane. The obtained spectrum of the Crab nebula validates the present version of our response matrix. The 3 $\sigma$ sensitivity of the instrument at 1 MeV is 8 10$^{-7}$ph$\cdot$cm$^{-2}\cdot$s$^{-1}\cdot$keV$^{-1}$ for the continuum and 3 10$^{-5}$ph$\cdot$cm$^{-2}\cdot$s$^{-1}$ for narrow lines.Comment: 10 pages, 18 figures, accepted for publication in A&A (special INTEGRAL volume