Fast reconstruction of degenerate populations of conductance-based neuron models from spike times
This addresses the problem of connecting neuronal spike data to underlying molecular mechanisms for neuroscientists, representing a novel method for a known bottleneck.
The paper tackled the challenge of linking spike timing to ion channel composition in neurons by developing a method that uses deep learning and Dynamic Input Conductances to infer interpretable components from spike times and generate populations of neuron models that replicate observed activity, achieving fast and accurate results with only spike recordings.
Neurons communicate through spikes, and spike timing is a crucial part of neuronal processing. Spike times can be recorded experimentally both intracellularly and extracellularly, and are the main output of state-of-the-art neural probes. On the other hand, neuronal activity is controlled at the molecular level by the currents generated by many different transmembrane proteins called ion channels. Connecting spike timing to ion channel composition remains an arduous task to date. To address this challenge, we developed a method that combines deep learning with a theoretical tool called Dynamic Input Conductances (DICs), which reduce the complexity of ion channel interactions into three interpretable components describing how neurons spike. Our approach uses deep learning to infer DICs directly from spike times and then generates populations of "twin" neuron models that replicate the observed activity while capturing natural variability in membrane channel composition. The method is fast, accurate, and works using only spike recordings. We also provide open-source software with a graphical interface, making it accessible to researchers without programming expertise.