@article {67721,
	title = {Electrode pooling can boost the yield of extracellular recordings with switchable silicon probes.},
	journal = {Nature Communications},
	volume = {12},
	year = {2021},
	month = {2021 Sep 02},
	pages = {5245},
	abstract = {<p>State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.</p>
},
	keywords = {Action Potentials, Animals, Brain, Electrodes, Implanted, Electrophysiology, Equipment Design, Extracellular Space, Mice, Nerve Net, Neurons, Signal Processing, Computer-Assisted, Silicon},
	issn = {2041-1723},
	doi = {10.1038/s41467-021-25443-4},
	author = {Lee, Kyu Hyun and Ni, Yu-Li and Colonell, Jennifer and Karsh, Bill and Putzeys, Jan and Pachitariu, Marius and Harris, Timothy D and Meister, Markus}
}
@conference {48833,
	title = {Extracting regions of interest from biological images with convolutional sparse block coding.},
	booktitle = {Neural Information Processing Systems (NIPS 2013)},
	year = {2013},
	month = {2013 Dec 05},
	address = {Lake Tahoe},
	abstract = {<p>Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.</p>
},
	url = {http://papers.nips.cc/paper/5167-extracting-regions-of-interest-from-biological-images-with-convolutional-sparse-block-coding},
	author = {Pachitariu, Marius and Packer, Adam M and Pettit, Noah and Dalgleish, Henry and H{\"a}usser, Michael and Sahani, Maneesh}
}
