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Automated Detection of Extracellular Action Potentials Propagation and Short Latency Coupling


Multi-electrode arrays (MEAs) non-invasively record extracellular action potentials (eAPs, also known as spikes) from hundreds of neurons simultaneously. We developed two algorithms that work with recordings from such devices. The first algorithm allows for automated detection of action potential propagation. Since extracellular electrodes sample from the local electrical field, each electrode can detect eAPs from multiple nearby neurons. One method to assign eAPs to their source neurons is to use spike sorting, a computational process that groups eAPs from single `units' based on assumptions of how spike waveforms correlate with different neuronal sources, to interpret spike trains at individual electrodes of high-density arrays. However, when experimental conditions result in changes to eAP waveforms, spike sorting routines may have difficulty correlating eAPs from multiple neurons at single electrodes before and after such waveform changes. We present here a novel, empirical method for unambiguously isolating eAPs from individual, uniquely identifiable neurons, based on automated multi-point detection of action potential propagation. This method is insensitive to changes in eAP waveform morphology because it makes no assumptions about the relationship between spike waveform and neuronal source. Our algorithm for automated detection of action potential propagation produces a `fingerprint' that uniquely identifies those spikes from each source neuron. By unambiguously isolating eAPs from multiple neurons in each recording, on a range of platforms and experimental preparations, our method now enables high-content screening with contemporary MEAs. We outline the limitations and strengths of propagation-based isolation of eAPs from single neurons and propose how our automated method complements spike sorting and could be adapted to in vivo use. Our second algorithm uses the information extracted from the first algorithm to non-invasively detect synaptic relationships among neurons from in vitro networks. Our methods identify short latency spiking relationships between neurons with properties expected of synaptically coupled neurons, namely they were recapitulated by direct stimulation and were sensitive to changing the number of active synaptic sites. Our methods enabled us to assemble a functional subset of neuronal connectivity in our cultures.

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