Governments with concerns about growing demented populations are funding brain research. One idea is to map all the connections in the brain (the connectome) as if this were a feasible goal that would contribute to understanding how the brain works. Another idea (delusional for sure) was to attempt simulation the human brain in a supercomputer. The European Union had pledged €1-billion for the Human Brain Project (HBP). In October 2013, a group of neuroscientists were unhappy, claiming that poor management altered their ” scientific plans.” Alison Abbott reported: “The HBP was originally designed to promote digital technologies by supporting and learning from neuroscience. A key element of the project was to develop supercomputers that neuroscientists will use to try to simulate the brain. But as the initiative has developed, its goal has become more and more diffuse. And after months of often fractious discussions about the program’s scientific scope, tempers boiled over at the end of May (2014), when the HBP’s three-man executive board decided to cut parts of the project, including one on cognitive neuroscience, from the second phase — in a manner that the signatories say was autocratic and scientifically inappropriate.”[i] For my money, the idea of “building a computational model of the human brain" should not be funded at all since no-one understands how the brain achieves its impressive array of functions; simulating a complex organ with computer programming is futile when you do not understand what you are simulating.
The USA has another Brain Initiative with 4.5 billion USD promised over 5 years. The budget may well be reduced by administrations to follow, but at least, American scientists are enthusiastic. [ii] A working group, designing the goals and methods of the brain initiative wrote:" The human brain is the source of our thoughts, emotions, perceptions, actions, and memories; it confers on us the abilities that make us human, while simultaneously making each of us unique. Over recent years, neuroscience has advanced to the level that we can envision a comprehensive understanding of the brain in action, spanning molecules, cells, circuits, systems, and behavior. This vision, in turn, inspired the BRAIN Initiative. On April 2, 2013, President Obama launched the BRAIN Initiative to “accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact.” [iii] One can be forgiven to treat all megaprojects with lofty goals with considerable skepticism. The least convincing movement attached to brain science claims that computers can simulate brain function and will rival human intelligence soon. This nonsense has gained both popular approval and also corporate funding from big money corporations such as Google.
New words and phrases proliferate in and around neuroscience projects. Connectome is a questionable name for all the “connections” made by cells in the brain. The word suggests an assumption that just by mapping connections the functions of the brain will be revealed. Esther Landhuis summarized the enormous task of obtaining and sharing brain data. She stated that neuroscientists are starting to share and integrate data — but shifting to a team approach isn't easy. She referred to an extraordinary accomplishment of a Taiwan research group:” Scientists there are studying the humble fruit fly, reverse-engineering its brain from images of single neurons. Their efforts have produced 3D maps of brain circuitry in stunning detail. The wiring diagrams look like colorful threads on a tapestry, and they're clear enough to show which cell clusters control specific behaviors. By stimulating a specific neural circuit, researchers can cue a fly to flap its left wing or swing its head from side to side. The team required a full decade to image 60,000 neurons, at a rate of 1 gigabyte per cell, says project leader Ann-Shyn Chiang and that's not even half of the nerve cells in the Drosophila brain. Using the same protocol to image the 86 billion neurons in the human brain would take an estimated 17 million years…Scientists can chart the brain at multiple levels.
The HCP seeks to map brain connectivity at a macroscopic scale, using magnetic resonance imaging (MRI). Some labs are mapping neural tracks at a microscopic level, whereas others, such as Chiang's, trace every synapse and neural branch with nanoscale precision. Still others are working to overlay gene-expression patterns, electrophysiological measurements or other functional data on those maps. The approaches use different methods — but all create big data. In part, this is because the brain, no matter the species, is so large and interconnected. But it also stems from the cells' unwieldy dimensions. A mammalian neuron's main extension — its axon — can be 200,000 times as long as its smallest branches, called dendrites, are wide. If a scale model were built such that spaghetti strands represented the dendrites, the neuron itself would be more than one-third of a kilometer long.
Engert of Harvard University in Cambridge stated that the information content in raw data is mostly irrelevant. He referred to with genome sequencing: before they had automated sequencers, researchers read DNA as ordered patterns of bands on polyacrylamide gels exposed to X-ray film. Now, computer algorithms convert those bands to a sequence of Gs, As, Ts and Cs — the bases that make up the DNA strands — and no one saves the original images. Similarly, brain scientists should focus not on curating and distributing raw data, but rather on developing algorithms to encode the information using fewer bits. Ideally, he says, such algorithms would enable the microscopes that collect the data to compress them as well. The idea is sensible, but could prove challenging for the brain, in part because of mathematics. To determine protein structure using X-ray crystallography, for example, there's a “really clean theoretical model” — a series of equations that relates specific characteristics of a protein to quantifiable features in its diffraction pattern, says Greg Farber manages the US National Institute of Mental Health (NIMH) data archive to work out the 3D structure, you'd just measure the intensities of the spots. You don't need to keep the many other pixels of data on that film. Neuroscientists have no comparable model — no map that associates neural connectivity and activity with behavior, memory or cognition. Given the brain's immense intricacy, Farber says, the problem is not that we have too much data, but that we don't have nearly enough for the complexity we're trying to address”.[iv]
Brain Network DynamicsThere are many attempts to develop a theory of or at least to understand the dynamic principles or brain function. Vidaurrea et al developed a model of brain network patterns. They wrote: The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. Robust patterns of synchronized activity can be measured in the brain in both task and rest. The spatial organization of these patterns at rest has been studied well. In this study, we focus on the temporal organization of the dynamics of the resting-state networks, and discover a temporal hierarchy in which brain networks are organized into two distinct sets, or metastates. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.” (Diego Vidaurrea et al.Brain network dynamics are hierarchically organized in time. PNAS April 3 2017.)
[i] Abbot, A. Row hits flagship brain plan. Nature 511, 133–134 (10 July 2014)
[iii] . USA NIH Brain Initiative. Brain 2025 June 5, 2014.
[iv] Esther Landhuis. Neuroscience: Big brain, big data. Nature 541, 559–561. 26 January 2017