JuliaCon 2017

Neurons throughout the brain, and particularly in the cerebral cortex, represent many quantities of interest using population codes. Latent variable models of neural population activity may be seen as attempting to identify the value, time-evolution and encoding of such internal variables from neural data alone. They do so by seeking a small set of underlying processes that can account for the coordinated activity of the population.introduce a novel estimation method [1] for latent factor models for point processes that operates on continuous spike times. Our method is based on score matching for point process regressions [2] adapted to population recordings with latent processes formed by mixing basis functions.basis functions are represented as either Fourier modes, or functions living in a Reproducing Kernel Hilbert Space, parametrised using MLKernels. The method requires the kernel matrix as well as the first and second derivatives thereof, which we can compute efficiently via the Calculus package, making use of anonymous functions. Parameter estimation is then closed form and thus lightning fast up to normalisation, but afterwards we need to estimate the total intensity in the observation period. The approximation of the time integral relies on Cubature.jl.to its speed, this method enables neuroscientists to visualise latent processes in real time during experimental recordings and immediate compare them to their expectations, thus quickening the Planning-Design-Analysis loop by a large margin.1. https://github.com/gbohner/PoissonProcessEstimation.jl2. Sahani, M; Bohner, G and Meyer A, 2016 - Score-matching estimators for continuous-time point-process regression models. MLSP2016