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- Open Access
Estimating the fraction of falsely detected spikes in high density microelectrode array recordings based on correlations
© Muthmann et al; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Neuronal Activity
- Spike Train
- Electrical Coupling
- Spike Time
- Spontaneous Firing
High-density microelectrode arrays (MEA) can measure neuronal activity in potentially thousands of units with a high spatial resolution . However due to the small size of the preamplifers, noise artifacts can affect spike detection. Additionally, the MEA chip itself is not perfectly homogeneous and the electrical coupling between the electrodes and a neuron may be weak. Therefore, the characteristics of neuronal spikes and noise are inherently different in each recording channel, such that estimating an average performance of the spike detection would not be representative for individual recording channels. As we aim to observe slow changes in single neuron activity, it is crucial to know how much a change in electrical coupling could potentially affect the number of detected spikes.
Here we estimate the quality of spike detection using correlations as an indicator to distinguish between neuronal activity and noise. First we use a threshold-based detection of putative spikes, with a deliberately low threshold to lower the number of undetected spikes. We estimate pairwise correlations between spike trains based on spike ranks rather than time in order to reduce the effects of nonstationarities in the recordings. We then assume that the frequency of spontaneous firing in the neurons is small compared to the evoked firing and check for each detected spike whether we can find correlated units which are active within a short interval around its spike time. For each unit, we compare the relative frequency of such correlated spikes to the frequency that would be obtained by doing the same analysis with a Poisson spike train.
This work was supported by the Erasmus Mundus EuroSPIN programme (JOM) and MRC Fellowship G0900425 (MHH).
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