@article {70373,
	title = {Raster photostimulation of large-scale neural populations},
	journal = {bioRxiv},
	year = {2026},
	month = {2026 Apr 24},
	abstract = {<p>Neural computations are implemented by distributed neural populations that often span multiple brain areas. Causal photo-activation experiments done simultaneously with neural recordings can greatly improve our understanding of these computations, but such methods are typically limited to small subsets of neurons in restricted fields of view. Here we describe a new system called raster photostimulation for photo-activating and recording thousands of neurons, over a short 300 ms time window and over a large 5 mm field-of-view on a two-photon mesoscope. The photo-activation is precisely matched to the neural recording configuration, as it uses the same optical path, although with a different laser that is independently gated. We demonstrate pixel-level precision, frame-by-frame mask updating, and single-frame photostimulation of thousands of neurons. While this method lacks the precise temporal control of alternative methods, it compensates with ease-of-use, spatial precision, cost of implementation and by pushing the limits on the number of near-simultaneously stimulated neurons.</p>
},
	doi = {10.64898/2026.04.21.719951},
	url = {https://www.biorxiv.org/content/early/2026/04/24/2026.04.21.719951},
	author = {LaFosse, Paul K. and Flickinger, Daniel and Jaindl, Georg and Drinnenberg, Antonia and Gr{\o}dem, Sverre and Lensj{\o}, Kristian K. and Ramakrishnan, Charu and Siverts, La{\textquoteright}Akea and Zeng, Hongkui and Tasic, Bosiljka and Daigle, Tanya L. and Fyhn, Marianne and Deisseroth, Karl and Stringer, Carsen 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}
}
