@article {68665,
	title = {Fast and sensitive GCaMP calcium indicators for imaging neural populations.},
	journal = {Nature},
	year = {2023},
	month = {2023 Mar 15},
	abstract = {<p>Calcium imaging with protein-based indicators is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators. The resulting {\textquoteright}jGCaMP8{\textquoteright} sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.</p>
},
	issn = {1476-4687},
	doi = {10.1038/s41586-023-05828-9},
	author = {Zhang, Yan and Rozsa, Marton and Liang, Yajie and Bushey, Daniel and Wei, Ziqiang and Zheng, Jihong and Reep, Daniel and Broussard, Gerard Joey and Tsang, Arthur and Tsegaye, Getahun and Narayan, Sujatha and Obara, Christopher J and Lim, Jing-Xuan and Patel, Ronak and Zhang, Rongwei and Ahrens, Misha B and Turner, Glenn C and Wang, Samuel S-H and Korff, Wyatt L and Schreiter, Eric R and Svoboda, Karel and Hasseman, Jeremy P and Kolb, Ilya and Looger, Loren L}
}
@article {5078,
	title = {From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization.},
	journal = {Journal of Neuroscience},
	volume = {32},
	year = {2012},
	month = {2012 Aug 15},
	pages = {11228-40},
	abstract = {<p>Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a "discrete-slot" model in which memory items are stored in a limited number of slots, and a "shared-resource" model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.</p>
},
	issn = {1529-2401},
	doi = {10.1523/​JNEUROSCI.0735-12.2012},
	author = {Wei, Ziqiang and Wang, Xiao-Jing and Wang, Da-Hui}
}
