“On latitudes this low the sun sets orthogonally to the horizon”—such is theopening line of Methyl Orange. “With all its vertical velocity it moved quicklybeneath the edge of the world down towards another,” the book continues. Thatthe sun sets more quickly the closer one gets to the equatorial line, is rarelyappraciated by the peoples there, since it is a quality only made aparent bycomparison to a pooint much further towars the poles—an expression rarelyafforded to most.That cartography has a place in geography is a truism. Ineed, perhaps the firstthing we ever had to navigate was the world, with all its hills, valeys, treasureouspaths, rivers, and land marks, etc. Much of this is done by the amygduly, a nearthe brain stem, part of the primitivae brain, indicating the primodial need fornavigation of landscapes. The history of war is largely one of landscapes, readany memoir of a battle. Futher, the expresison geography is destiny aludes tothe importance of the geographic world. Farely self explanatory. Making therules that govern the shape of the world excplit has been a prerequisite for ourmodern world. Much has been writting about the relationships between mapsand terretories [1]. The most similar geographical counterpart to the cerebralcortex is perhaps Vale da Lua in Goiás, Brazil.The 1999 atlas of the brain fsaverage “Neurons that fire together, wire together”is a frequent adage in neuroscience, often followed by its corollary “out of sync,lose your link”. That the brain is a network of neurons is, at this point, a truism.The quote posits that if neurons are active at the same time, their connectionstrengthens, and by the corollary, if they are not, their connection weakens.“Connection” in this context can be thought of as how much one neuroninfluences another. Mathematically, a neuron’s activation can be thought of aweighted sum of the activations of its neighbors. We are then asked to imaginea network in which nodes are occasionally active, and in which connectionsbetween active nodes tend to strengthen. Suppose then attaching certain nodesto the outside world, having their activation depend not on other neurons, but onexternal stimuli (light, sound, whatever), and attaching other nodes to actuators,things that move in the world. Transforming the adage into computation yieldsa system that then “does well” in the world. In the context of artificial inteligence(AI), we call this Hebbian learning, the namesake of Donald Hebb, who the quoteis therefore often misattributed to in the AI community.A truism as “the brain is a network of neurons” might be, there is, however, somewiggle room in its meaning: It is perhaps almost as well known that we cannotyet simulate a brain, or monitor it on the neuron level. And yet, that is wherethis network exists, nerve cells connected by synapses, axons, dendrites, and soon, communicating chemically with neurotransmitters, electrically with actionpotentials. It is a dynamic system, the most complex known to us. The mostsimilar system we have in AI is perhaps spiking neural networks, (SNNs) withtheir time-dependent activations, relatively trivially implemented in softwarelike spyx [2]. Does the inaccessibility of the brain neuron-level network meanthe network science is reserved for the largely abstract parts of neuroscience?The short answer is NO. The brain can be thought of as a network on a varietyof levels [3]. A good approximation of variety in this context is three:1.Microscale: The network of neurons, synapses, and neurotransmitters.2.Mesoscale: The network of brain regions, connected by structural connectivity.3.Macroscale: The network of brain regions, connected by functional connectivity.Nodes in the latter two domains might be regions of brain matter in cubic micrometers, cubic millimeters, or even centimeters. What then are connections?One answer is to take the afore mentioned adage as scripture, and computecorrelation coefcicients between voxels (pixel like cubes of brain activiationsscanned by MRI machines). Doing so, successfully allows us to reconstruct,forexample, what people are looking at from fMRI scans alone [4], [5]. Understanding how to best construct connectomes on these high levels is an ongoingproject [6]. For more on this see /neuroscopeReferences[1]B. Fischl, M. I. Sereno, R. B. Tootell, and A. M. Dale, “High-ResolutionIntersubject Averaging and a Coordinate System for the Cortical Surface,”Human Brain Mapping, vol. 8, no. 4, pp. 272–284, 1999, doi: 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4.[2]K. Heckel, S. Abreu, G. Lenz, T. Nowotny, and neworderofjamie, “Kmheckel/Spyx: V0.1.20.” Aug. 2024. doi: 10.5281/zenodo.13329958.[3]H. Kennedy, D. C. Van Essen, and Y. Christen, Eds., Micro-, Meso- and Macro-Connectomics of the Brain. Cham (CH): Springer, 2016.[4]A. T. Gifford et al., “The Algonauts Project 2023 Challenge: How the HumanBrain Makes Sense of Natural Scenes,” no. arXiv:2301.03198. arXiv, Jan. 2023.[5]E. J. Allen et al., “A Massive 7T fMRI Dataset to Bridge Cognitive Neuroscience and Artificial Intelligence,” Nature Neuroscience, vol. 25, no. 1, pp.116–126, Jan. 2022, doi: 10.1038/s41593-021-00962-x.[6]L. Coletta, M. Pagani, J. D. Whitesell, J. A. Harris, B. Bernhardt, and A.Gozzi, “Network Structure of the Mouse Brain Connectome with VoxelResolution,” Science Advances, vol. 6, no. 51, p. eabb7187, Dec. 2020, doi:10.1126/sciadv.abb7187.