@article {67225, title = {Comparative single-cell transcriptomics of complete insect nervous systems}, journal = {bioRxiv}, year = {2020}, month = {01/2020}, abstract = {

Molecular profiles of neurons influence information processing, but bridging the gap between genes, circuits, and behavior has been very difficult. Furthermore, the behavioral state of an animal continuously changes across development and as a result of sensory experience. How behavioral state influences molecular cell state is poorly understood. Here we present a complete atlas of the Drosophila larval central nervous system composed of over 200,000 single cells across four developmental stages. We develop polyseq, a python package, to perform cell-type analyses. We use single-molecule RNA-FISH to validate our scRNAseq findings. To investigate how internal state affects cell state, we optogentically altered internal state with high-throughput behavior protocols designed to mimic wasp sting and over activation of the memory system. We found nervous system-wide and neuron-specific gene expression changes. This resource is valuable for developmental biology and neuroscience, and it advances our understanding of how genes, neurons, and circuits generate behavior.

}, doi = {10.1101/785931}, url = {https://www.biorxiv.org/content/early/2020/01/16/785931}, author = {Cocanougher, Benjamin T. and Wittenbach, Jason D. and Long, Xi Salina and Kohn, Andrea B. and Norekian, Tigran P. and Yan, Jinyao and Colonell, Jennifer and Masson, Jean-Baptiste and Truman, James W. and Cardona, Albert and Turaga, Srinivas C. and Singer, Robert H. and Moroz, Leonid L. and Zlatic, Marta} } @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} } @proceedings {48942, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit.}, year = {2017}, month = {2017 Dec 04}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://papers.nips.cc/paper/6940-model-based-bayesian-inference-of-neural-activity-and-connectivity-from-all-optical-interrogation-of-a-neural-circuit}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguaye, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C} } @conference {67221, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit}, booktitle = {Advances in Neural Information Processing Systems}, year = {2017}, publisher = {Curran Associates, Inc.}, organization = {Curran Associates, Inc.}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://discovery.ucl.ac.uk/id/eprint/10072143/}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguay, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} }