SPySort: Neuronal Spike Sorting with Python

Extracellular recordings with multi-electrode arrays is one of the basic tools of contemporary neuroscience. These recordings are mostly used to monitor the activities, understood as sequences of emitted action potentials, of many individual neurons. But the raw data produced by extracellular recordings are most commonly a mixture of activities from several neurons. In order to get the activities of the individual contributing neurons, a pre-processing step called spike sorting is required. We present here a pure Python implementation of a well tested spike sorting procedure. The latter was designed in a modular way in order to favour a smooth transition from an interactive sorting, for instance with IPython, to an automatic one. Surprisingly enough - or sadly enough, depending on one's view point -, recoding our now 15 years old procedure into Python was the occasion of major methodological improvements.Comment: Part of the Proceedings of the 7th European Conference on Python in Science (EuroSciPy 2014), Pierre de Buyl and Nelle Varoquaux editors, (2014

Structure of receptive fields in a computational model of area 3b of primary sensory cortex

International audienceIn a previous work, we introduced a computational model of area 3b which is built upon the neural field theory and receives input from a simplified model of the index distal finger pad populated by a random set of touch receptors (Merkell cells). This model has been shown to be able to self-organize following the random stimulation of the finger pad model and to cope, to some extent, with cortical or skin lesions. The main hypothesis of the model is that learning of skin representations occurs at the thalamo-cortical level while cortico-cortical connections serve a stereotyped competition mechanism that shapes the receptive fields. To further assess this hypothesis and the validity of the model, we reproduced in this article the exact experimental protocol of DiCarlo et al. that has been used to examine the structure of receptive fields in area 3b of the primary somatosensory cortex. Using the same analysis toolset, the model yields consistent results, having most of the receptive fields to contain a single region of excitation and one to several regions of inhibition. We further proceeded our study using a dynamic competition that deeply influences the formation of the receptive fields. We hypothesized this dynamic competition to correspond to some form of somatosensory attention that may help to precisely shape the receptive fields. To test this hypothesis, we designed a protocol where an arbitrary region of interest is delineated on the index distal finger pad and we either (1) instructed explicitly the model to attend to this region (simulating an attentional signal) (2) preferentially trained the model on this region or (3) combined the two aforementioned protocols simultaneously. Results tend to confirm that dynamic competition leads to shrunken receptive fields and its joint interaction with intensive training promotes a massive receptive fields migration and shrinkage

Intact model.

<p><b>A</b> Evolution of the receptive field of neuron during learning. The neuron is initially silent (epoch 0) but learns quickly to answer to a large range of stimuli (epoch 1500) until finally settling on a narrower range of stimuli. <b>B</b> Receptive fields of the whole model. Each blue circle represents a neuron. The center of the circle indicates the (converted) receptive field center and the radius expresses the (relative) size of the receptive field. <b>C</b> Response of the model (after learning) to a set of 10×10 regularly spaced stimuli. Each square represent a response to a specific stimulus. <b>D</b> This represents the mean evolution of thalamo-cortical weights of neuron during learning (i.e. ). <b>E</b> & <b>F</b> Histogram of receptive field sizes (100 bins) before (E) and after (F) learning. The final distribution is Gaussian-shaped centered around a mean value of . Is is to be noted the high number of very small receptive field size that correspond to neurons on the border of the field that are mostly silent during the whole simulation.</p

Cortical lesion type III (red area).

<p><b>A</b> Evolution of the receptive field of neuron during retraining after a cortical lesion of type III. This particular neuron has expanded its RF immediately after lesion and moreover it has has replaced his preferred location as it is depicted at the final profile (epoch ). <b>B</b> Receptive fields of the whole model. The cortical lesion is appeared at the preferred locations since the previously corresponding neurons are now affected by the lesion. The RFs around the lesion have been increased in size comparing with the corresponding pre-lesion <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040257#pone-0040257-g006" target="_blank">figure 6<b>B</b></a>. <b>C</b> Response of the model (after retraining) to a set of 10×10 regularly spaced stimuli. <b>D</b> This represents the mean evolution of thalamo-cortical weights of neuron during retraining (i.e. ). <b>E</b> & <b>F</b> Histogram of receptive field sizes (100 bins) before (E) and after (F) skin lesion. The initial distribution is Gaussian-shaped centered around a mean value of . However, the final distribution is a Uniform-like centered around a mean value of . This uniform-like distribution indicates the existence of neurons whose RFs have underwent an expansion, but not a shrinkage as in cortical lesion type I case.</p

Locations of training and validation stimuli on the skin patch.

<p><b>A</b> Training is performed on a set of 16×16 stimuli that are uniformly distributed over the area (skin patch normalized area is ) such that any stimulus is entirely located on the skin patch (see example stimulus on upper left corner). <b>B</b> Validation (as reported in <b>C</b> panels in result figures) is performed on a set of 10×10 stimuli that are uniformly distributed over the area.</p

Response of the model and lateral excitation.

<p>The response of the model and the amount of lateral excitation at a specific site (center of activity). Plots represent the response profile corresponding to the dashed lines. <b>A</b> Before learning. <b>B</b> After learning.</p

Receptive fields of the intact model.

<p><b>A</b> Of the whole cortical sheet. <b>B</b> Magnification of the white box.</p

Cortical lesion type I (red area).

<p><b>A</b> Evolution of the receptive field of neuron during retraining after a cortical lesion of type I. Immediately following the lesion (epoch ), RF tends to expand. This phenomenon persists until the final epoch is reached. <b>B</b> Receptive fields of the whole model. <b>C</b> Response of the model (after retraining) to a set of 10×10 regularly spaced stimuli. The activity of the model is now bound to the unlesioned area. <b>D</b> This represents the mean evolution of thalamo-cortical weights of neuron during retraining (i.e. ). <b>E</b> & <b>F</b> Histogram of receptive field sizes (100 bins) before (E) and after (F) skin lesion. The initial distribution is Gaussian-shaped centered around a mean value of . However, the final distribution is a uniform-like centered around a mean value of (). This uniform-like distribution indicates the existence of neurons whose RFs have underwent an expansion, but not a shrinkage.</p