Workshops

1. Spike Sorting: with Samuel Garcia and Pierre Yger

Accessing the activity of large ensemble of neurons is a crucial challenge in neuroscience, and in recent years, Multi-Electrode Arrays (MEA) and large silicon probes have been developed to record simultaneously from hundreds of electrodes packed with a high density, both in vivo and in vitro. With these devices, each electrode records the extracellular field in its vicinity and can detect the action potentials (or spikes) emitted by the neighboring neurons in the tissue. In contrast to intracellular recording, those extracellular recordings do not give a direct access to the neuronal activity: one needs to process the recorded signals to extract the spikes emitted by the different cells around the electrode. This complex problem of source separation is also termed “spike sorting”. While various solutions for small number of channels (tens at max) can be found in the large literature on spike sorting algorithms, these new devices with thousands of channels challenge the classical way to do spike sorting. In this workshop, we will present you an overview of the canonical workflow used to perform spike sorting, dissecting all its the computational bottlenecks. Some recent algorithms/framework such as SpyKING CIRCUS, tridesclous or SpikeINterface will be presented to gain a better understanding of what tool you should use for your own data and/or scientific question. The workshop will include a hands-on session, so do not hesitate to bring your own data!

To register:

neuralnet2019-workshop@sciencesconf.org

Contacts:

samuel.garcia@cnrs.fr

pierre.yger@inserm.fr

 

2. Theoretical/Computational Modelling:

Given the increasing complexity of neural data and the generalized use of theoretical models in neuroscience, more and more neuroscientists rely on computationnal modelling tools. I will provide a short introductory lecture on the contribution of theoretical models to neuroscience. The methods used in modelling will be illustrated through two examples. First, I'll show how network dynamics can be derived formally in a simplified model of an E-I neural network. Then, we will conduct the simulation of an integrate-and-fire neuron and derive its I-F curve for various levels of synaptic noise using Python language and Jupyter notebook environment.

To register:

neuralnet2019-workshop@sciencesconf.org

Contacts:

arthur.leblois@u-bordeaux.fr

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