@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}
}
@article {6580,
	title = {Nonlinear dynamics support a linear population code in a retinal target-tracking circuit.},
	journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience},
	volume = {33},
	year = {2013},
	month = {2013 Oct 23},
	pages = {16971-82},
	abstract = {<p>A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.</p>
},
	issn = {1529-2401},
	doi = {10.1523/JNEUROSCI.2257-13.2013},
	author = {Leonardo, Anthony and Meister, Markus}
}
