Neuronal Signal Analysis & Modeling

High-density microelectrode array (HD-MEA) recordings provide large amounts of data. Signal processing techniques are needed to cope with the large data volume, to extract the relevant features and information and to gain new insights into how neuronal ensembles process information. The obtained results then can be used as input for a detailed modeling of neuronal behavior.

Enlarged view: Spike sorting of HD-MEA recordings
Spike Sorting of HD-MEA Recordings: Exemplary recording of 6 neighboring electrodes out of total of 102 on a HD-MEA (left); zoom-in on two spikes (right);

The HD-MEA systems capture simultaneous spiking activities of multiple neurons. Therefore, spikes in the data have to be extracted and assigned to individual neurons, a process usually referred to as external pagespike sorting. However, the large number of channels, the highly redundant nature of HD-MEA data, and the large data volume render this task particularly challenging. We are, therefore, developing new algorithms and strategies to perform spike sorting on hundreds or thousands of recording channels in a reliable and efficient way, so that the experimenter will be able to analyze the acquired data within the duration of an experiment and will have the opportunity to immediately react to particular features in the acquired data.

Enlarged view: Spike sorting of HD-MEA recordings
Spike Sorting of HD-MEA Recordings: Superimposed spike traces of the two neurons (green and blue) on electrodes 3 and 5 (left), the footprints of which spatially overlap (right).

Moreover, we use the acquired high-spatiotemporal-resolution data as input and to improve compartmental neuronal models. We aim at a better understanding of the correlation between intracellular and extracellular features of action potentials, and we try to improve neuronal models.

Movie Purkinje Cell Action Potential: Development of a single Purkinje cell action potential at high-spatiotemporal resolution as recorded with HD-MEAs. Left: 3D animation. Right: 2D HD-MEA recordings.

Collaborations

Friedrich-Miescher Institute, Basel, Switzerland; Ecole Nationale Supérieure, Paris, France;

Recent publications

M. Engelene Obien, A. Hierlemann, U. Frey, "Accurate signal-source localization in brain slices by means of high-density microelectrode arrays", Scientific Reports 2019, Vol. 9, Article 788 (DOI: 10.1038/s41598-018-36895-y). external pageOnline

R. Diggelmann, M. Fiscella, A. Hierlemann, F. Franke, "Automatic Spike Sorting Algorithm for High-Density Microelectrode Arrays", Journal of Neurophysiology 2018, 120 (6), pp. 3155–3171 (DOI: 10.1152/jn.00803.2017). external pageOnline

A. Drinnenberg, F. Franke, R. K. Morikawa, J. Jüttner, D. Hillier, P. Hantz, A. Hierlemann, R. A. da Silveira, B. Roska, "How diverse retinal functions arise from feedback at the first visual synapse", Neuron 2018, Vol. 99 (1), pp. 117-134.e11 (DOI: 10.1016/j.neuron.2018.06.001). external pageOnline / external pagePreview

F. Franke, M. Fiscella, M. Sevelev, B. Roska, A. Hierlemann, R. Azeredo da Silveira, "Structures of neural correlation and how they favor coding", Neuron, 2016, 89(2), pp. 409-422 (DOI: 10.1016/j.neuron.2015.12.037). external pageOnline

J. Dragas, D. Jäckel, A. Hierlemann, F. Franke, "Complexity Optimisation and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm", IEEE Trans. On Neural Systems and Rehabilitation Engineering, 2015, Vol. 23(2), pp. 149-158 (DOI: 10.1109/TNSRE.2014.2370510). external pageOnline

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