@article {68656,
	title = {Connectome-constrained networks predict neural activity across the fly visual system},
	journal = {Nature},
	year = {2024},
	month = {2024 Sep 11},
	abstract = {<p>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.</p>
},
	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}
}
@booklet {67224,
	title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy},
	year = {2020},
	abstract = {<p>Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.</p>
},
	url = {https://arxiv.org/abs/1907.00770},
	author = {Artur Speiser and Lucas-Raphael M{\"u}ller and Ulf Matti and Christopher J. Obara and Wesley R. Legant and Jonas Ries and Jakob H. Macke and Srinivas C. Turaga}
}
