Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area.

Face expertise is a pivotal social skill. Developmental prosopagnosia (DP), i.e.

Datasets for calcium dynamics comparison between the whole-cell and a β-escin based perforated patch configuration in brain slices from adult mice.

Multiple processes shape calcium signals in neurons. The spatial and temporal dynamics of these signals are determined by various cellular parameters, including the calcium influx, calcium buffering, and calcium extrusion. The different Ca handling properties can be estimated using the 'added buffer approach' [1], which is based on a single compartment model of Ca buffering.

A simple method for getting standard error on the ratiometric calcium estimator.

The ratiometric fluorescent calcium indicator Fura-2 plays a fundamental role in the investigation of cellular calcium dynamics. Despite of its widespread use in the last 30 years, only one publication (Joucla et al., 2010)) proposed a way of obtaining confidence intervals on fitted calcium dynamic model parameters from single 'calcium transients'.

Analysis of neuronal Ca handling properties by combining perforated patch clamp recordings and the added buffer approach.

Ca functions as an important intracellular signal for a wide range of cellular processes. These processes are selectively activated by controlled spatiotemporal dynamics of the free cytosolic Ca. Intracellular Ca dynamics are regulated by numerous cellular parameters.

Transfer functions linking neural calcium to single voxel functional ultrasound signal.

Functional ultrasound imaging (fUS) is an emerging technique that detects changes of cerebral blood volume triggered by brain activation. Here, we investigate the extent to which fUS faithfully reports local neuronal activation by combining fUS and two-photon microscopy (2PM) in a co-registered single voxel brain volume. Using a machine-learning approach, we compute and validate transfer functions between dendritic calcium signals of specific neurons and vascular signals measured at both microscopic (2PM) and mesoscopic (fUS) levels.

Energy imbalance alters Ca handling and excitability of POMC neurons.

Satiety-signaling, pro-opiomelanocortin (POMC)-expressing neurons in the arcuate nucleus of the hypothalamus play a pivotal role in the regulation of energy homeostasis. Recent studies reported altered mitochondrial dynamics and decreased mitochondria- endoplasmic reticulum contacts in POMC neurons during diet-induced obesity. Since mitochondria play a crucial role in Ca signaling, we investigated whether obesity alters Ca handling of these neurons in mice.

Toward standard practices for sharing computer code and programs in neuroscience.

Computational techniques are central in many areas of neuroscience, and are relatively easy to share. This paper describes why computer programs underlying scientific publications should be shared, and lists simple steps for sharing. Together with ongoing efforts in data sharing, this should aid reproducibility of research.

Estimating background-subtracted fluorescence transients in calcium imaging experiments: a quantitative approach.

Calcium imaging has become a routine technique in neuroscience for subcellular to network level investigations. The fast progresses in the development of new indicators and imaging techniques call for dedicated reliable analysis methods. In particular, efficient and quantitative background fluorescence subtraction routines would be beneficial to most of the calcium imaging research field.

Towards reliable spike-train recordings from thousands of neurons with multielectrodes.

The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided.

Making neurophysiological data analysis reproducible: why and how?

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.

Quantitative estimation of calcium dynamics from ratiometric measurements: a direct, nonratioing method.

Measuring variations of intracellular free calcium concentration through the changes in fluorescence of a calcium-sensitive dye is a ubiquitous technique in neuroscience. Despite its popularity, confidence intervals (CIs) on the estimated parameters of calcium dynamics models are seldom given. To address this issue, we have developed a two-stage model for ratiometric measurements obtained with a charge-coupled device (CCD) camera.

Differences of Ca2+ handling properties in identified central olfactory neurons of the antennal lobe.

Information processing in neurons depends on highly localized Ca2+ signals. The spatial and temporal dynamics of these signals are determined by a variety of cellular parameters including the calcium influx, calcium buffering and calcium extrusion. Our long-term goal is to better understand how intracellular Ca2+ dynamics are controlled and contribute to information processing in defined interneurons of the insect olfactory system.

Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR.

Multi-electrode arrays (MEA) allow experimentalists to record extracellularly from many neurons simultaneously for long durations. They therefore often require that the data analyst spends a considerable amount of time first sorting the spikes, then doing again and again the same basic analysis on the different spike trains isolated from the raw data. This spike train analysis also often generates a considerable amount of figures, mainly diagnostic plots, that need to be stored (and/or printed) and organized for efficient subsequent use.

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings.

Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution.

Axonal speeding: shaping synaptic potentials in small neurons by the axonal membrane compartment.

The role of the axonal membrane compartment in synaptic integration is usually neglected. We show here that in interneurons of the cerebellar molecular layer, where dendrites are so short that the somatodendritic domain can be considered isopotential, the axonal membrane contributes a significant part of the cell input capacitance. We examine the impact of axonal membrane on synaptic integration by cutting the axon with two-photon illumination.

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 multi-modal inter-spike interval (ISI) histograms and to correlations between successive ISIs.

Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach.

Spike-sorting techniques attempt to classify a series of noisy electrical waveforms according to the identity of the neurons that generated them. Existing techniques perform this classification ignoring several properties of actual neurons that can ultimately improve classification performance. In this study, we propose a more realistic spike train generation model.

Using noise signature to optimize spike-sorting and to assess neuronal classification quality.

We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons.