@article {68656, title = {Connectome-constrained networks predict neural activity across the fly visual system}, journal = {Nature}, year = {2024}, month = {2024 Sep 11}, abstract = {

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected-a universally observed feature of biological neural networks across species and brain regions.

}, doi = {10.1038/s41586-024-07939-3}, url = {https://doi.org/10.1038/s41586-024-07939-3}, author = {Janne K. Lappalainen and Fabian D. Tschopp and Sridhama Prakhya and Mason McGill and Aljoscha Nern and Kazunori Shinomiya and Shin-ya Takemura and Eyal Gruntman and Jakob H. Macke and Srinivas C. Turaga} } @conference {67973, title = {Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity}, booktitle = {International Conference on Learning Representations}, year = {2022}, month = {09/2021}, abstract = {

The availability of both anatomical connectivity and brain-wide neural activity measurements in C. elegans make the worm a promising system for learning detailed, mechanistic models of an entire nervous system in a data-driven way. However, one faces several challenges when constructing such a model. We often do not have direct experimental access to important modeling details such as single-neuron dynamics and the signs and strengths of the synaptic connectivity. Further, neural activity can only be measured in a subset of neurons, often indirectly via calcium imaging, and significant trial-to-trial variability has been observed. To address these challenges, we introduce a connectome-constrained latent variable model (CC-LVM) of the unobserved voltage dynamics of the entire C. elegans nervous system and the observed calcium signals. We used the framework of variational autoencoders to fit parameters of the mechanistic simulation constituting the generative model of the LVM to calcium imaging observations. A variational approximate posterior distribution over latent voltage traces for all neurons is efficiently inferred using an inference network, and constrained by a prior distribution given by the biophysical simulation of neural dynamics. We applied this model to an experimental whole-brain dataset, and found that connectomic constraints enable our LVM to predict the activity of neurons whose activity were withheld significantly better than models unconstrained by a connectome. We explored models with different degrees of biophysical detail, and found that models with realistic conductance-based synapses provide markedly better predictions than current-based synapses for this system.

}, url = {https://openreview.net/forum?id=CJzi3dRlJE-}, author = {Lu Mi and Richard Xu and Sridhama Prakhya and Albert Lin and Nir Shavit and Aravinthan Samuel and Srinivas C Turaga} } @article {67223, title = {Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data}, journal = {CoRR}, volume = {abs/1906.03214}, year = {2019}, month = {10/2019}, abstract = {

The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here, we derive importance weighted extensions to Adversarial Variational Bayes (AVB) and Adversarial Autoencoder (AAE). These latent variable models use implicitly defined inference networks whose approximate posterior density qφ(z|x) cannot be directly evaluated, an essential ingredient for importance weighting. We show improved training and inference in latent variable models with our adversarially trained importance weighting method, and derive new theoretical connections between adversarial generative model training criteria and marginal likelihood based methods. We apply these methods to the important problem of inferring spiking neural activity from calcium imaging data, a challenging posterior inference problem in neuroscience, and show that posterior samples from the adversarial methods outperform factorized posteriors used in VAEs.

}, url = {http://arxiv.org/abs/1906.03214}, author = {Daniel Jiwoong Im and Sridhama Prakhya and Jinyao Yan and Srinivas C. Turaga and Kristin Branson} }