Single cell electrophysiology remains one of the most widely used approaches of systems
neuroscience. Decisions made by the experimenter during electrophysiology recording
largely determine recording quality, duration of the project and value of the collected
data. Therefore, online feedback aiding these decisions can lower monetary and time
investment, and substantially speed up projects as well as allow novel studies otherwise
not possible due to prohibitively low throughput. Real-time feedback is especially
important in studies that involve optogenetic cell type identification by enabling
a systematic search for neurons of interest. However, such tools are scarce and limited
to costly commercial systems with high degree of specialization, which hitherto prevented
wide-ranging benefits for the community. To address this, we present an open-source
tool that enables online feedback during electrophysiology experiments and provides
a Python interface for the widely used Open Ephys open source data acquisition system.
Specifically, our software allows flexible online visualization of spike alignment
to external events, called the online peri-event time histogram (OPETH). These external
events, conveyed by digital logic signals, may indicate photostimulation time stamps
for in vivo optogenetic cell type identification or the times of behaviorally relevant
events during in vivo behavioral neurophysiology experiments. Therefore, OPETH allows
real-time identification of genetically defined neuron types or behaviorally responsive
populations. By allowing "hunting" for neurons of interest, OPETH significantly reduces
experiment time and thus increases the efficiency of experiments that combine in vivo
electrophysiology with behavior or optogenetic tagging of neurons.
