Efficient spike-sorting of multi-state neurons using inter-spike intervals information

We demonstrate the efficacy of a new spike-sorting method based on a Markov Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multimodal interspike interval (ISI) histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an "active" state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spike-sorting methods. We first show that a Hidden Markov Model with 3 log-Normal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spike-sorting algorithm (Pouzat et al, 2004, J. Neurophys. 91, 2910-2928) and test this new algorithm on multi-unit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patch-clamp pipette.Comment: 25 pages, to be published in Journal of Neurocience Method

The EU trade policy is an important contribution to overcome slow economic growth in the EU countries. Challenge this point of view.

As it has been suggested EU trade policy contribution to economic growth of MSs should be considered at best moderate if not negligible – on one hand due to still extensively applied extra-tariff measures such as anti-dumping proceedings and safety and health procedures, on the other hand because of lack of direct link between liberalisation of trade in industrial commodities and stimulation of growth. All this seems quite logical if one takes under consideration the fact that EU trade policy objectives are to less extent aimed at promotion of growth rather than at achieving social and political goals

The EU trade policy is an important contribution to overcome slow economic growth in the EU countries. Challenge this point of view.

As it has been suggested EU trade policy contribution to economic growth of MSs should be considered at best moderate if not negligible – on one hand due to still extensively applied extra-tariff measures such as anti-dumping proceedings and safety and health procedures, on the other hand because of lack of direct link between liberalisation of trade in industrial commodities and stimulation of growth. All this seems quite logical if one takes under consideration the fact that EU trade policy objectives are to less extent aimed at promotion of growth rather than at achieving social and political goals

Making neurophysiological data analysis reproducible. Why and how?

Manuscript submitted to "The Journal of Physiology (Paris)". Second version.Reproducible data analysis is an approach aiming at complementing classical printed scientific articles with everything required to independently reproduce the results they present. ''Everything'' covers here: the data, the computer codes and a precise description of how the code was applied to the data. A brief history of this approach is presented first, starting with what economists have been calling replication since the early eighties to end with what is now called reproducible research in computational data analysis oriented fields like statistics and signal processing. Since efficient tools are instrumental for a routine implementation of these approaches, a description of some of the available ones is presented next. A toy example demonstrates then the use of two open source software for reproducible data analysis: the ''Sweave family'' and the org-mode of emacs. The former is bound to R while the latter can be used with R, Matlab, Python and many more ''generalist'' data processing software. Both solutions can be used with Unix-like, Windows and Mac families of operating systems. It is argued that neuroscientists could communicate much more efficiently their results by adopting the reproducible research paradigm from their lab books all the way to their articles, thesis and books

Une approche Monte Carlo par Chaînes de Markov pour la classification des potentiels d'action. <br />Application à l'étude des corrélations d'activité des cellules de Purkinje.

To be fully exploitable extracellular multi-unit data have to be sorted out into several single neuron spike trains: this particular data processing is called "spike-sorting". This work is a contribution to the development and the carrying out of an automatic spike-sorting method implementing a Markov Chain Monte Carlo (MCMC) method. The proposed method enables the experimentalist to take into account the occurrence times of spikes, in addition to the information provided by their waveforms, to perform spike-sorting. This use of temporal information makes it possible to automatically identify neurons with non-stationary spike waveforms. It also improves the separation of neurons whose spike waveforms are similar. This methodological work led to the release of a free software documented by its user guide.This spike-sorting method has been experimentally validated on populations of Purkinje cells (PCs) in rat cerebellar slices. Besides, the spike train analysis of these multiple cells data did not reveal any significant temporal correlations between spontaneous PC spike trains, in spite of common inhibition of PCs by molecular layer interneurons and direct inhibition PC to PC. Simulations showed that the influence of these inhibitions onto temporal relations between spike trains is too weak to be detected with our correlation analysis. Codes written to analyse spike trains are also released as a second software.Pour être réellement exploitables, les données d'enregistrements extracellulaires multiunitaires doivent faire l'objet d'un traitement préalable visant à isoler les activités neuronales individuelles qui les constituent: le spike-sorting. Ce travail de thèse est une contribution au développement et à la réalisation d'une méthode automatique de spike-sorting implémentant un algorithme de Monte Carlo par Chaînes de Markov (MCMC). La méthode proposée permet de tenir compte, en plus de la forme des potentiels d'action (PAs), de l'information fournie par leurs temps d'émission pour réaliser la classification. Cette utilisation de l'information temporelle rend possible l'identification automatique de neurones émettant des PAs de formes non stationnaires. Elle améliore aussi grandement la séparation de neurones aux PAs de formes similaires. Ce travail méthodologique à débouché sur la création d'un logiciel libre accompagné de son manuel d'utilisateur.Cette méthode de spike-sorting a fait l'objet d'une validation expérimentale sur des populations de cellules de Purkinje (PCs), dans les tranches de cervelet de rat. Par ailleurs, l'étude des trains de PAs de ces cellules fournis par le spike-sorting, n'a pas révélé de corrélations temporelles significatives en régime spontané, en dépit de l'existence d'une inhibition commune par les interneurones de la couche moléculaire et d'une inhibition directe de PC à PC. Des simulations ont montré que l'influence de ces inhibitions sur les relations temporelles entre les trains de PCs était trop faible pour pouvoir être détectée par nos méthodes d'analyse de corrélations. Les codes élaborés pour l'analyse des trains de PAs sont également disponibles sous la forme d'un second logiciel libre

Une approche Monte Carlo par chaînes de Markov pour la classification des potentiels d' action (application à l' étude des corrélations d' activité des cellules de Purkinje)

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Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."

<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p