@article {70286,
	title = {Extracting large-scale neural activity with Suite2p},
	journal = {bioRxiv},
	year = {2026},
	month = {2026 Feb 06},
	abstract = {<p>Neural recordings using optical methods have improved dramatically. For example, we demonstrate here recordings of over 100,000 neurons from the mouse cortex obtained with a standard commercial microscope. To process such large datasets, we developed Suite2p, a collection of efficient algorithms for motion correction, cell detection, activity extraction and quality control. We also developed new approaches to benchmark performance on these tasks. Our GPU-accelerated non-rigid motion correction substantially outperforms alternative methods, while running over five times faster. For cell detection, Suite2p outperforms the CNMF algorithm in Caiman and Fiola, finding more cells and producing fewer false positives, while running in a fraction of the time. We also introduce quality control steps for users to evaluate performance on their own data, while offering alternative algorithms for specialized types of recordings such as those from one-photon and voltage imaging.</p>
},
	doi = {10.64898/2026.02.04.703741},
	url = {https://www.biorxiv.org/content/early/2026/02/06/2026.02.04.703741},
	author = {Stringer, Carsen and Ki, Chris and DelGrosso, Nicholas and LaFosse, Paul and Zhang, Qingqing and Pachitariu, Marius}
}
@article {70117,
	title = {Spatial predictive coding in visual cortical neurons},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Sep 19},
	abstract = {<p>Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial predictive coding, much of the recent experimental data has been interpreted as evidence for temporal predictive coding. Here we directly tested whether the {\textquotedblleft}mismatch{\textquotedblright} neural responses in sensory cortex are due to a spatial or a temporal internal model. We adopted two common paradigms to study predictive coding: one based on virtual-reality and one based on static images. After training mice with repeated visual stimulation for several days, we performed multiple manipulations, including: 1) we introduced a novel stimulus, 2) we replaced a stimulus with a novel gray wall, 3) we duplicated a trained stimulus, or 4) we altered the order of the stimuli. The first two manipulations induced a substantial mismatch response in neural populations of up to 20,000 neurons recorded across primary and higher-order visual cortex, while the third and fourth ones did not. Thus, a mismatch response only occurred if a new spatial {\textendash} not temporal {\textendash} pattern was introduced.</p>
},
	doi = {10.1101/2025.09.17.676794},
	url = {https://www.biorxiv.org/content/early/2025/09/19/2025.09.17.676794},
	author = {Zhang, Qingqing and Gr{\o}dem, Sverre and Gracias, Alexa and Lensj{\o}, Kristian K. and Fyhn, Marianne and Stringer, Carsen and Pachitariu, Marius}
}
