The Experts below are selected from a list of 19503 Experts worldwide ranked by ideXlab platform

Felix Franke - One of the best experts on this subject based on the ideXlab platform.

  • visapy a python tool for biophysics based generation of virtual spiking activity for evaluation of spike Sorting Algorithms
    Journal of Neuroscience Methods, 2015
    Co-Authors: Espen Hagen, Gaute T Einevoll, Felix Franke, Torbjorn V Ness, Amir Khosrowshahi, Christina Sorensen, Marianne Fyhn, Torkel Hafting
    Abstract:

    Abstract Background New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike Sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. New method We here present a general simulation tool for computing benchmarking data for evaluation of spike-Sorting Algorithms entitled ViSAPy (Virtual Spiking Activity in Python) . The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy . Results ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. Comparison with existing methods ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. Conclusion ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.

  • complexity optimization and high throughput low latency hardware implementation of a multi electrode spike Sorting algorithm
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015
    Co-Authors: Jelena Dragas, David Jackel, Andreas Hierlemann, Felix Franke
    Abstract:

    Reliable real-time low-latency spike Sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-Sorting Algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency.

  • Towards reliable spike-train recordings from thousands of neurons with multielectrodes.
    Current Opinion in Neurobiology, 2012
    Co-Authors: Gaute T Einevoll, Felix Franke, Espen Hagen, Christophe Pouzat, Kenneth D Harris
    Abstract:

    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. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-Sorting Algorithms.

Gaute T Einevoll - One of the best experts on this subject based on the ideXlab platform.

  • visapy a python tool for biophysics based generation of virtual spiking activity for evaluation of spike Sorting Algorithms
    Journal of Neuroscience Methods, 2015
    Co-Authors: Espen Hagen, Gaute T Einevoll, Felix Franke, Torbjorn V Ness, Amir Khosrowshahi, Christina Sorensen, Marianne Fyhn, Torkel Hafting
    Abstract:

    Abstract Background New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike Sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. New method We here present a general simulation tool for computing benchmarking data for evaluation of spike-Sorting Algorithms entitled ViSAPy (Virtual Spiking Activity in Python) . The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy . Results ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. Comparison with existing methods ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. Conclusion ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.

  • Towards reliable spike-train recordings from thousands of neurons with multielectrodes.
    Current Opinion in Neurobiology, 2012
    Co-Authors: Gaute T Einevoll, Felix Franke, Espen Hagen, Christophe Pouzat, Kenneth D Harris
    Abstract:

    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. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-Sorting Algorithms.

Alexander Bertrand - One of the best experts on this subject based on the ideXlab platform.

  • SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance
    Neuroinformatics, 2020
    Co-Authors: Jasper Wouters, Fabian Kloosterman, Alexander Bertrand
    Abstract:

    Spike Sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike Sorting Algorithms have been published. In an effort to guide a user towards a specific spike Sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike Sorting Algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those Algorithms.

Olivier Marre - One of the best experts on this subject based on the ideXlab platform.

  • Recent progress in multi-electrode spike Sorting methods
    Journal of Physiology - Paris, 2017
    Co-Authors: Baptiste Lefebvre, Pierre Yger, Olivier Marre
    Abstract:

    In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing Algorithms. Algorithms need to solve a source separation problem, also known as spike Sorting. However, these new devices challenge the classical way to do spike Sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these Algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike Sorting Algorithms.

Espen Hagen - One of the best experts on this subject based on the ideXlab platform.

  • visapy a python tool for biophysics based generation of virtual spiking activity for evaluation of spike Sorting Algorithms
    Journal of Neuroscience Methods, 2015
    Co-Authors: Espen Hagen, Gaute T Einevoll, Felix Franke, Torbjorn V Ness, Amir Khosrowshahi, Christina Sorensen, Marianne Fyhn, Torkel Hafting
    Abstract:

    Abstract Background New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike Sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. New method We here present a general simulation tool for computing benchmarking data for evaluation of spike-Sorting Algorithms entitled ViSAPy (Virtual Spiking Activity in Python) . The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy . Results ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. Comparison with existing methods ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. Conclusion ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.

  • Towards reliable spike-train recordings from thousands of neurons with multielectrodes.
    Current Opinion in Neurobiology, 2012
    Co-Authors: Gaute T Einevoll, Felix Franke, Espen Hagen, Christophe Pouzat, Kenneth D Harris
    Abstract:

    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. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-Sorting Algorithms.