Using deep neural networks to detect complex spikes of cerebellar Purkinje Cells

AbstractOne of the most powerful excitatory synapses in the entire brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called “complex spike”, at the level of the postsynaptic Purkinje cell, comprising of a fast initial spike of large amplitude followed by a slow polyphasic tail of small amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events are extremely rare, only occurring 1-2 times per second. As a result, drawing conclusions about their functional role has been very challenging, as even few errors in their detection may change the result. Since standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection of long traces of Purkinje cell recordings by experts. Here we present a supervised deep learning algorithm for rapidly and reliably detecting complex spikes as an alternative to tedious visual inspection. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spike events much faster than human experts, but it also excavates key features of complex spike morphology with a performance comparable to that of such experts.Significance statementClimbing fiber driven “complex spikes”, fired at perplexingly low rates, are known to play a crucial role in cerebellum-based motor control. Careful interpretations of these spikes require researchers to manually detect them, since conventional online or offline spike sorting algorithms (optimized for analyzing the much more frequent “simple spikes”) cannot be fully trusted. Here, we present a deep learning approach for identifying complex spikes, which is trained on local field and action potential recordings from cerebellar Purkinje cells. Our algorithm successfully identifies complex spikes, along with additional relevant neurophysiological features, with an accuracy level matching that of human experts, yet with very little time expenditure.