@article {28486,
	title = {Inferring causal connectivity from pairwise recordings and optogenetics},
	journal = {BioRxiv},
	year = {2022},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {To study how the brain works mechanistically, neuroscientists want to quantify causal interactions between neurons, typically requiring perturbations. When using optogenetic interventions, multiple neurons are usually perturbed which produces a confound {\textendash} any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which of the neurons produced the causal effect. Here we show how such confounds produce large biases, and we explain how they can be reduced by combining instrumental variable (IV) and difference in differences (DiD) techniques from econometrics. The interaction between stimulation and the absolute refractory period of the neuron produces a weak, approximately random signal which can be exploited to estimate causal transmission probability. On simulated neural networks, we find that estimates using ideas from IV and DiD outperform na{\"\i}ve techniques suggesting that methods from causal inference can be useful when studying neural interactions.},
	keywords = {causal inference, connectivity, Neuroscience},
	author = {Lepper{\o}d, Mikkel and St{\"o}ber, Tristan and Hafting, Torkel and Fyhn, Marianne and Kording, Konrad}
}
@article {28371,
	title = {A thousand brains: toward biologically constrained AI},
	journal = {SN Applied Sciences},
	volume = {3},
	number = {8},
	year = {2021},
	month = {07/2021},
	pages = {743},
	publisher = { Springer nature},
	abstract = {This paper reviews the state of artificial intelligence (AI) and the quest to create general AI with human-like cognitive capabilities. Although existing AI methods have produced powerful applications that outperform humans in specific bounded domains, these techniques have fundamental limitations that hinder the creation of general intelligent systems. In parallel, over the last few decades, an explosion of experimental techniques in neuroscience has significantly increased our understanding of the human brain. This review argues that improvements in current AI using mathematical or logical techniques are unlikely to lead to general AI. Instead, the AI community should incorporate neuroscience discoveries about the neocortex, the human brain{\textquoteright}s center of intelligence. The article explains the limitations of current AI techniques. It then focuses on the biologically constrained Thousand Brains Theory describing the neocortex{\textquoteright}s computational principles. Future AI systems can incorporate these principles to overcome the stated limitations of current systems. Finally, the article concludes that AI researchers and neuroscientists should work together on specified topics to achieve biologically constrained AI with human-like capabilities.},
	keywords = {Biologically constrained general AI, Common cortical algorithm, General AI, Limitations of narrow AI, Neocortex, Neuroscience},
	doi = { https://doi.org/10.1007/s42452-021-04715-0},
	author = {Kjell J{\o}rgen {Hole} and Ahmad, Subutai}
}
@article {27435,
	title = {Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions},
	journal = {Frontiers in Applied Mathematics and Statistics},
	volume = {6},
	number = {41},
	year = {2020},
	month = {Aug-09-2020},
	publisher = {Frontiers},
	keywords = {CANDECOMP/PARAFAC, independent component analysis (ICA), local field potential (LFP), Neuroscience, population rate model, principal component analysis (PCA), tensor decompositions},
	doi = {10.3389/fams.2020.00041},
	url = {https://www.frontiersin.org/article/10.3389/fams.2020.00041/fullhttps://www.frontiersin.org/article/10.3389/fams.2020.00041/full},
	author = {Geddes, Justen and Gaute T. {Einevoll} and Evrim Acar {Ataman} and Alexander J. {Stasik}}
}
@misc {24828,
	title = {The waterscape of the brain},
	year = {2016},
	month = {09/2016},
	keywords = {Neuroscience, Numerical Simulations, Waterscape},
	author = {Piersanti, Eleonora and Marie E. {Rognes}}
}
