Spike sorting of synchronous spikes from local neuron ensembles

Abstract

Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons.

Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study

“…Franke et al have also similarly incorporated the spatial and temporal information to resolve overlapping spikes from neurons in the brain [19].…”

Synchrony, connectivity, and functional similarity in auditory midbrain local circuits

“…It is especially difficult when the goal is examining sharply synchronous responses. The main difficulty is resolving overlapping spike waveforms, which is an unresolved problem with few viable approaches (Pillow et al, 2013, Franke et al, 2015). Our approach used an established algorithm that allowed us to first recover two spike waveform templates, based on a large number of well-separated spiking events, and then use these templates to decompose the relatively small numbers of overlapping spike events (Lewicki, 1994).…”

“…The spike sorting algorithm also allowed for the proper classification of spikes even if they overlapped with an action potential of another neuron. This is a challenging problem for spike-sorting algorithms, and even tetrode recordings cannot resolve this issue (Franke et al, 2015). In a third step of our approach, which utilizes the procedure of (Lewicki, 1994), an overlap decomposition procedure is performed when a potential spike event cannot be assigned to a well-characterized single unit and may be the result of two spike waveform overlapping in time.…”

Model-based spike sorting with a mixture of drifting t -distributions

“…We tested the hybrid Bayes-optimal template matching algorithm (Franke et al, 2015). This method maintains a high detection rate regardless of inter-spike interval (Figure 4E, dashed line), but required careful tuning of the detection threshold to achieve an acceptable level of false positives.…”

“…For the template-matching approach analyzed in Figure 4E, we used the hybrid Bayes-optimal template matching algorithm (Franke et al, 2015). This template matching algorithm assumes homoscedasticity, which allows for efficient computation of the relative posterior likelihood, and uses a “hybrid” approach combining a partial enumeration of the potential overlaps with iterative greedy refinement.…”