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Approaches to predicting neuronal spike responses commonly use a Poissonlearning objective. This objective quantizes responses into spike counts withina fixed summation interval, typically on the order of 10 to 100 milliseconds induration; however, neuronal responses are often time accurate down to a fewmilliseconds, and Poisson models struggle to precisely model them at thesetimescales. We propose the concept of a spike distance function that mapspoints in time to the temporal distance to the nearest spike. We show thatneural networks can be trained to approximate spike distance functions, and wepresent an efficient algorithm for inferring spike trains from the outputs ofthese models. Using recordings of chicken and frog retinal ganglion cellsresponding to visual stimuli, we compare the performance of our approach tothat of Poisson models trained with various summation intervals. We show thatour approach outperforms the use of Poisson models at spike train inference.

Spike distance function as a learning objective for spike prediction