“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.That the sun sets more quickly the closer one gets to the equatorial line, israrely appraciated by the peoples there, since it is a quality only made aparentby comparison 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, treasureous paths, rivers, and land marks, etc. Much of this is done by the amygduly, anear the brain stem, part of the primitivae brain, indicating the primodial needfor navigation of landscapes. The history of war is largely one of landscapes,read any memoir of a battle. Futher, the expresison geography is destiny aludesto the 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, wiretogether” 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, atthis point, a truism. The quote posits that if neurons are active at the sametime, their connection strengthens, and by the corollary, if they are not, theirconnection weakens. “Connection” in this context can be thought of as howmuch one neuron influences another. Mathematically, a neuron’s activationcan be thought of a weighted sum of the activations of its neighbors. We arethen asked to imagine a network in which nodes are occasionally active, andin which connections between active nodes tend to strengthen. Suppose thenattaching certain nodes to the outside world, having their activation dependnot on other neurons, but on external stimuli (light, sound, whatever), and attaching other nodes to actuators, things that move in the world. Transformingthe adage into computation yields a system that then “does well” in the world.In the context of artificial inteligence (AI), we call this Hebbian learning, thenamesake of Donald Hebb, who the quote is therefore often misattributed to inthe AI community.A truism as “the brain is a network of neurons” might be, there is, however,some wiggle room in its meaning: It is perhaps almost as well known thatwe cannot yet simulate a brain, or monitor it on the neuron level. And yet,that is where this network exists, nerve cells connected by synapses, axons,dendrites, and so on, communicating chemically with neurotransmitters, electrically with action potentials. It is a dynamic system, the most complex knownto us. The most similar system we have in AI is perhaps spiking neural networks,(SNNs) with their time-dependent activations, relatively trivially implementedin software like spyx [2]. Does the inaccessibility of the brain neuron-levelnetwork mean the network science is reserved for the largely abstract parts ofneuroscience? The short answer is NO. The brain can be thought of as a networkon a variety of levels [3]. A good approximation of variety in this context isthree: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. M. Heckel and T. Nowotny, “Spyx: A Library for Just-In-Time CompiledOptimization of Spiking Neural Networks.” Feb. 2024.[3]H. Kennedy, D. C. Van Essen, and Y. Christen, Eds., Micro-, Meso- andMacro-Connectomics of the Brain. Cham (CH): Springer, 2016.[4]A. T. Gifford et al., “The Algonauts Project 2023 Challenge: How the Human Brain 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.