@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 {69557,
	title = {Bridging tuning and invariance with equivariant neuronal representations},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Aug 02},
	abstract = {<p>As we move through the world, we see the same visual scenes from different perspectives. Although we experience perspective deformations, our perception of a scene remains stable. This raises the question of which neuronal representations in visual brain areas are perspective-tuned and which are invariant. Focusing on planar rotations, we introduce a mathematical framework based on the principle of equivariance, which asserts that an image rotation results in a corresponding rotation of neuronal representations, to explain how the same representation can range from being fully tuned to fully invariant. We applied this framework to large-scale simultaneous neuronal recordings from four visual cortical areas in mice, where we found that representations are both tuned and invariant but become more invariant across higher-order areas. While common deep convolutional neural networks show similar trends in orientation-invariance across layers, they are not rotation-equivariant. We propose that equivariance is a prevalent computation of populations of biological neurons to gradually achieve invariance through structured tuning.</p>
},
	doi = {10.1101/2024.08.02.606398},
	url = {https://www.doi.org/10.1101/2024.08.02.606398},
	author = {Hoeller, Judith and Zhong, Lin and Pachitariu, Marius and Romani, Sandro}
}
