Neuronal Signal Analysis

Recent advancements in  HD-MEA technology and the integration of  different recording modalities lead to large-volume data sets. The high  spatiotemporal resolution of HD-MEA data offers the potential to  elucidate functional properties of neuronal networks across multiple  scales, ranging from subcellular compartments through individual neurons
to entire networks.  As 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. The obtained results then can also be used as input for a detailed modeling of neuronal behavior.

Enlarged view: Signal processing of HD-MEA recordings
Signal processing of HD-MEA recording encompassing data processing with spike sorting and feature extraction. Relevant features include spike waveform features, activity features, and network features.

The extracellular data, recorded with HD-MEAs from neuronal networks or tissue, consist of local field potentials (LFPs) and action potentials (APs, or "spikes"). LFPs can be extracted from the recordings by filtering signals below ~300 Hz and mostly reflect the combined activity of neurons within a few hundred micrometers of the electrode. In contrast, signals between 300 Hz and 6 kHz represent multi-unit activity (MUA) and include APs of neurons within tens of micrometers of the electrode.

As the electrodes of HD-MEA systems simultaneously capture spiking activities of multiple neurons, spikes in the data have to be extracted and assigned to individual neurons, a process usually referred to as external page spike sorting. However, the large number of channels, the highly redundant nature of HD-MEA data, and the large data volume render this task challenging. We are, therefore, working on techniques and strategies to perform spike sorting on data of hundreds or thousands of recording channels in a reliable and efficient way.

Finally, 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.

Relevant publications

A. Buccino, T. Damart, J. Bartram, D. Mandge, X. Xue, M. Zbili, T. Gänswein, A. Jaquier, V. Emmenegger, H. Markram, A. Hierlemann, W. Van Geit, "A multimodal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays", Neural Computation 2024, 36, p. 1286-1331 (DOI: 10.1162/neco_a_01672). external page Online

C. Donner, J. Bartram, P. Hornauer, T. Kim, D. Roqueiro, A. Hierlemann, G. Obozinski, M. Schröter, "Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains", PLOS Computational Biology 2024, 20 (4), Article e1011964. external page Online 

P. Hornauer, G. Prack, N. Anastasi, S. Ronchi, T. Kim, C. Donner, M. Fiscella, K. Borgwardt, V. Taylor, R. Jagasia, D. Roqueiro, A. Hierlemann, M. Schröter, "DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks", Stem Cell Reports 2023, 19 (2), p. 285-298 (DOI: 10.1016/j.stemcr.2023.12.008). external page Online

S. Kumar, T. Gänswein, A. Buccino, X. Xue, J. Bartram, V. Emmenegger, A. Hierlemann, "Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study", Frontiers in Neuroinformatics 2022, 16, Article 957255 (DOI: 10.3389/fninf.2022.957255). external page Online

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 page Online

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