@proceedings {48805, title = {Fast amortized inference of neural activity from calcium imaging data with variational autoencoders.}, year = {2017}, month = {2017 Dec 04}, abstract = {

Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Using the framework of variational autoencoders, we propose to amortize inference by training a deep neural network to perform model inversion efficiently. The recognition network is trained to produce samples from the posterior distribution over spike trains. Once trained, performing inference amounts to a fast single forward pass through the network, without the need for iterative optimization or sampling. We show that amortization can be applied flexibly to a wide range of nonlinear generative models and significantly improves upon the state of the art in computation time, while achieving competitive accuracy. Our framework is also able to represent posterior distributions over spike-trains. We demonstrate the generality of our method by proposing the first probabilistic approach for separating backpropagating action potentials from putative synaptic inputs in calcium imaging of dendritic spines.\ 

}, url = {http://www.mackelab.org/pubs/Speiser_NIPS_2017.pdf}, author = {Speiser, Artur and Yan, Jinyao and Archer, Evan and Buesing, Lars and Turaga, Srinivas C and Macke, Jakob H} } @article {46575, title = {Inferring neural population dynamics from multiple partial recordings of the same neural circuit.}, journal = {Advances in Neural Information Processing Systems (NIPS) }, volume = {26}, year = {2013}, month = {2013}, abstract = {

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs.

}, url = {http://papers.nips.cc/paper/4874-inferring-neural-population-dynamics-from-multiple-partial-recordings-of-the-same-neural-circuit}, author = {Turaga, Srinivas C and Buesing, Lars and Packer, Adam M. and Dalgleish, Henry and Pettit, Noah and H{\"a}usser, Michael and Macke, Jakob H.} }