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Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
Ben Shababo · Brooks Paige · Ari Pakman · Liam Paninski

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits. We base our procedure on data from an experiment in which populations of putative presynaptic neurons can be stimulated while a subthreshold recording is made from a single postsynaptic neuron. We present a realistic statistical model which accounts for the main sources of variability in this experiment and allows for large amounts of information about the biological system to be incorporated if available. We then present a simpler model to facilitate online experimental design which entails the use of efficient Bayesian inference. The optimized approach results in equal quality posterior estimates of the synaptic weights in roughly half the number of experimental trials under experimentally realistic conditions, tested on synthetic data generated from the full model.

Author Information

Ben Shababo (Columbia University)
Brooks Paige (Alan Turing Institute / University of Cambridge)
Ari Pakman (Columbia University)
Liam Paninski (Columbia University)

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