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Sorting synchronous spikes from 3-dimensional tetrodes


Franke,  F
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Munk,  MH
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Franke, F., Alle H, Meier P, Geiger J, Obermayer, K., & Munk, M. (2011). Sorting synchronous spikes from 3-dimensional tetrodes. Poster presented at 41st Annual Meeting of the Society for Neuroscience (Neuroscience 2011), Washington, DC, USA.

Cooperativity of cortical neurons often expresses in synchronous spike discharge. As neighboring neurons are more densely connected than neurons recorded from different micro electrodes, synchronous firing is more prevalent. Recording from local groups of neurons with conventional micro electrodes bears the difficulty that sorting the activity of spikes from different cells is error prone, because so far temporally overlapping spikes could not be separated reliably. Using tetrodes with a highly reproducible 3-dimensional structure, a single spike can be recorded on more than one channel. This provides additional information about the origin of the action potential ("stereo-effect") and can be used to improve sorting performance. In the case of overlapping spikes additional information provided by the stereo-effect can be even more valuable to detect and correctly resolve the overlap. Here, we present simultaneous intracellular and extracellular recordings of two neurons for the assessment of spike sorting performance. Two pyramidal neurons in slices of rat visual cortex were recorded intracellularly, stimulated in a way to produce synchronous or near synchronous spiking activity and also recorded extracellularly with a 3D multichannel electrode (“tetrode”). Spike sorting was carried out on the extracellular data only. The intracellular recording is then used to asses the sorting performance. The algorithm employed here uses linear filters derived from the prototypical spike waveforms ("templates"). The filter outputs can be interpreted in a Bayesian sense and are able to detect and resolve overlapping spikes. We show that - if for every neuron its template is estimated using the available ground truth information - overlapping spikes can be reliably separated with the same performance as non overlapping spikes (<5 error rate). Separation performance is greatly improved by the additional information provided by multi electrode recordings confirming the results for non overlapping spikes. The finding that classical clustering based methods perform poorly for overlapping spikes is also confirmed. Furthermore, the inability of ICA to resolve overlapping spikes is demonstrated, even if the correct independent component basis vectors are estimated with the available ground truth information. In conclusion, we show that the proposed methods can be used to successfully analyze synchronous and near synchronous spikes recorded from neurons at the same tetrode, both in-vitro and in macaque prefrontal cortex.