Neuronal Classification from Electrophysiological Recordings Based on Multimodal Deep Learning

A system for classifying neurons from extracellular electrophysiological recordings, the system comprising:

• a data interface configured to receive a plurality of extracellular electrophysiological recordings captured by at least one recording device, each recording representing extracellular neuronal activity of a plurality of neurons;
• and
• one or more processors coupled to a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
• extract, from at least one electrophysiological recording, for each of a plurality of neurons, (i) a waveform feature representing a shape of an extracellular action potential of the neuron, and (ii) an interspike-interval feature representing a temporal distribution of intervals between successive action potentials of each of the plurality of neurons;
• generate, using one or more conditional variational autoencoders conditioned on an recording source descriptor associated with the at least one recording device, (i) a waveform latent code determined from a waveform feature vector and (ii) a timing latent code determined from a spike-timing distribution feature vector;
• fuse the waveform latent code and the timing latent code to produce a multimodal latent embedding for a neuron unit;
• and
• perform one or more analysis operations on a multimodal latent representations to generate an analysis result characterizing the plurality of neurons.