Ari Pakman – Fast Penalized State-space Methods for Inferring Dendritic Synaptic Connectivity, 05/01/2012

We present fast methods for filtering voltage measurements and performing optimal inference of the location and strength of synaptic connections in large dendritic trees. Given noisy, subsampled voltage observations we develop fast L1-penalized regression methods for Kalman state-space models of the neuron voltage dynamics. The value of the L1-penalty parameter is chosen using cross-validation or, for low signal-to-noise ratio, a Mallows’ Cp-like criterion. Using low-rank approximations, we reduce the inference runtime from cubic to linear in the number of dendritic compartments. We illustrate our results with simulations on toy and real neuronal geometries, using different sampling schemes. Sampling fixed points in the dendritic tree leads to poor inference of the synaptic weights; much better results are obtained when the sampling points change in time, either in a scanned manner or using randomized sampling schemes.