Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
With the advent of modern stimulation techniques in neuroscience, the opportunity arises to map neuron to neuron connectivity. In this work, we develop a method for efficiently inferring posterior distributions over synaptic strengths in neural microcircuits. The input to our algorithm is data from experiments in which action potentials from putative presynaptic neurons can be evoked 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 significant prior information about the connectivity and neuronal cell types to be incorporated if available. Due to the technical challenges and sparsity of these systems, it is important to focus experimental time stimulating the neurons whose synaptic strength is most ambiguous, therefore we also develop an online optimal design algorithm for choosing which neurons to stimulate at each trial.
Targeted stimulation of multiple pre-synaptic cells 
      combined with intra-cellular recording of a single post-synaptic cell is a 
      popular and important method for estimating synaptic connectivity between 
      these cells. The main drawback of this approach is that it is technically 
      difficult, so that only small number of connections can be estimated in a 
      given experiment. The authors aim to provide statistical methods for 
      speeding up these experiments, by firstly providing a full statistical 
      model which takes the sparsity of neural coupling into account, and 
      secondly by providing computationally efficient methods for choosing 
      target-locations for stimulation. The paper is sound and clearly 
      explained, and the methods are evaluated using synthetic data. The 
      drawbacks of this submission are that a) no results on real data are 
      presented b) the neuron/noise model used is a bit simplistic (see details 
      below) and that it is therefore not quite clear how well this method will 
      work in a real experimental context. Overall, my verdict is that 
      this is a neat (but not outstanding) submission which could be a nice 
      addition to NIPS. Quality: The statistical model is reasonable and 
      the evaluation of the model on synthetic data is sound. However, there are 
      a number of properties of the model which might provide problematic on 
      real data, and therefore, the important test of the relevance of this 
      study will be how well it works in such a setting. In the absence of real 
      data, it would have been important to include simulation results in a more 
      realistic setting, i.e. one in which the model does not perfectly describe 
      the data (e.g. using a different neuron model, correlated noise, possibly 
      input from other neurons, feedback couplings, etc..) 
      Hyper-parameters: The statistical model includes some 
      hyper-parameters which probably strongly affect model performance, but 
      which are not set by the method. While it is true that some of these 
      parameters can be set by using known connection-probabilities, it is 
      unclear how appropriate this will be for a given experimental application. 
      How sensitive is the method to "wrong" hyperparameters e.g. on the 
      spike-and-slab prior?" Uncorrelated noise: The method assumes 
      uncorrelated noise on the membrane voltages, and says that it is 
      "straightforward" to extend this to correlated noise-- while I have no 
      doubts that it is straightforward mathematically, it would have been 
      important to see how strongly this affects performance e.g. in the 
      simulations. I could imagine that the inference task is substantially 
      harder if one also has to deal with correlated noise. Simulations: 
      In the absence of real data, it would have been important to at least 
      include simulation results in a more realistic setting, i.e. one in which 
      the model does not perfectly describe the data (e.g. using a different 
      neuron model, correlated noise, possibly input from other neurons, 
      feedback couplings, etc..). One of the results of the simulation was that 
      the experimental design strategy does not show a great performance benefit 
      in the high-noise regime with multiple stimulated entries-- I would expect 
      that this is actually the most important regime? Clarity: The 
      paper is mostly clearly written, but takes a long "run up" which sets up a 
      general statistical model which is then subsequently broken down to the 
      model which is actually used by the authors. (E.g. they write that 
      spike-and-slab priors with any distribution can be used, and later say 
      that they consider only the Gaussian case). I would suggest just 
      describing what was actually done to streamline the exposition." 
      Originality: As far as I know, this study is original. While 
      there have been previous studies that address estimation of synaptic 
      connectivity (from presynaptic stimulation/postsynaptic membrane 
      mesuarements (e.g. Hu, Leonardo, Chklovskii) this is the first such study 
      which aims to provide a "realistic" statistical model and, importantly, 
      which addresses the question of experimental design for this task. 
      Significance: The impact of this study will be be stronlgy 
      dependent on how well this method works on real data.