@article {69732,
	title = {A critical initialization for biological neural networks},
	journal = {bioRxiv},
	year = {2025},
	month = {01/2025},
	abstract = {<p>Artificial neural networks learn faster if they are initialized well. Good initializations can generate high-dimensional macroscopic dynamics with long timescales. It is not known if biological neural networks have similar properties. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice (two-photon and electrophysiology) are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was hippocampal area CA1: population activity in this area resembled an efficient, uncorrelated neural code, which may be optimized for information storage capacity. Global emergent activity modes persisted in simulations with sparse, clustered or spatial connectivity. We hypothesize that the spontaneous neural activity reflects a critical initialization of whole-brain neural circuits that is optimized for learning time-dependent tasks.</p>
},
	doi = {10.1101/2025.01.10.632397},
	url = {https://www.biorxiv.org/content/early/2025/01/10/2025.01.10.632397},
	author = {Pachitariu, Marius and Zhong, Lin and Gracias, Alexa and Minisi, Amanda and Lopez, Crystall and Stringer, Carsen}
}
@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}
}
