Image: Illustration by Mirko Ilic
In Brief
- A single neuron cannot do much, but string a few hundred together and a primitive nervous system emerges, one sophisticated enough to keep a worm going.
- More neurons equate to a more complex organism. A central preoccupation of neuroscience is deducing the way billions of neurons produce the human mind.
- Neuroscientists have begun to unravel the brain’s complexity by adopting research on other elaborate systems, ranging from computer chips to the stock market.
- Understanding the workings of the brain’s intricate networks may provide clues to the underlying origins of devastating disorders, including schizophrenia and dementia.
More In This Article
A single neuron sits in a petri dish, crackling in lonely contentment. From time to time, it spontaneously unleashes a wave of electric current that travels down its length. If you deliver pulses of electricity to one end of the cell, the neuron may respond with extra spikes of voltage. Bathe the neuron in various neurotransmitters, and you can alter the strength and timing of its electrical waves. On its own, in its dish, the neuron can’t do much. But join together 302 neurons, and they become a nervous system that can keep the worm Caenorhabditis elegans alive—sensing the animal’s surroundings, making decisions and issuing commands to the worm’s body. Join together 100 billion neurons—with 100 trillion connections—and you have yourself a human brain, capable of much, much more.
How our minds emerge from our flock of neurons remains deeply mysterious. It’s the kind of question that neuroscience, for all its triumphs, has been ill equipped to answer. Some neuroscientists dedicate their careers to the workings of individual neurons. Others choose a higher scale: they might, for example, look at how the hippocampus, a cluster of millions of neurons, encodes memories. Others might look at the brain at an even higher scale, observing all the regions that become active when we perform a particular task, such as reading or feeling fear. But few have tried to contemplate the brain on its many scales at once. Their reticence stems, in part, from the sheer scope of the challenge. The interactions between just a few neurons can be a confusing thicket of feedbacks. Add 100 billion more neurons to the problem, and the endeavor turns into a cosmic headache.
This article was originally published with the title 100 Trillion Connections.
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19 Comments
Add CommentI was relieved to see that neuroscientists speak in terms of interactions of elements/phenomena. As is always emphasized in statistics classes, the action is in the interactions, and, outside of stats and in more liberal arts classes, has been until the seeming assault on attention spans, the necessity of providing context for any statement rather than swallowing words made up to suit the audience. Interaction and context - that makes science (and living a thought-filled life)
Reply | Report Abuse | Link to thisWilliam Harvey Stroud
The concept of "more than the sum of its parts" may find demonstrable clarity in the 'mind' as complexity.
Reply | Report Abuse | Link to thiscf-At Home in the Universe - Stuart Kauffman
I've thought of consciousness as powered by the activity of the leftover energy our brain produces;its energy output decreases, we lose consciousness. Overly simple? As good as what we've got, and how daunting a task to gather, store, and analyze 100 trillion connections - you'd probably need something like a human brain for that task! Noble goals, I almost do not want them to succeed, I'd rather my consciousness be a mystery than a data set on a computer somewhere.
Reply | Report Abuse | Link to thisIn AWT the human brain is effectively simulator of hyperdimensional reality , which surrounds us.
Reply | Report Abuse | Link to thishttp://aetherwavetheory.blogspot.com/2010/01/awt-theory-of-human-conscioussness.html
The basic idea is, when we are doing rational decision, we are basically making simulation in high-dimensional causal space. So that the brain simulates this casual space by sound solitons spreading along neuron mesh and just collects the results. It means, both the abstract, both real concepts physically exist in our brain and they're capable to interact mutually like real objects.
The above model is similar to concept of "liquid computing developed by Swiss neuroscientist Henry Markram together with Graz University of Technology. In this model the brain works like a pond in which stones are thrown. The waves caused by this perturbation don't disappear immediately, but rather overlap with each other and collect information about how many stones were thrown in and how big they were. The main difference is just that the waves in the brain spread in a network of neurons and at very high speed of sound. I could say, we are maintaining acoustic, self-repairing and self-evolving version of optoelectronic W.I.K.I. brain from famous movie "I, robot".
Would it not be an interesting paradox that if, in the end, the human brain is simply incapeable of comprehending itself?
Reply | Report Abuse | Link to thisThe human brain is fundamentally incapable of comprehending the full complexity of any system, as it is, itself, a system of limited conceptual resources.
Reply | Report Abuse | Link to thisThis is not the only place you see small-world networks; indeed, our universe's underlying geometry follows mathematical rules similar to fractals/Mendelbulb symmetries that repeat the pattern of a system of weakly interacting sub-systems; ......each sub-system itself bound by stronger forces/influence/interactions. We see this pattern of weak and strong interactions forming local pockets of order out of a relatively vast expanse of apparent chaos everywhere we look: galaxy distribution at the super-cluster scale, solar systems, pattern replication in nature, the pockets of high-order complexity (human consciousnesses) interacting relatively weakly through social systems.
Reply | Report Abuse | Link to thisBTW, Benford's Law is another example to add, to the many stated in the article, of exponential/logarithmic patterns being inherently present in all systems in nature and human society; in the case of Benford's Law, defining the probabilistic distribution of numbers in a system that is defined to include multiple scales (powers). Exponential/logarithmic symmetry is indeed found everywhere a small-world network is.
"Creating models of the brain that can capture these dynamic networks will demand all the tricks of the trade that complexity theory can offer." Perhaps achieving understanding of such complexities will require more intellectual tricks of the trade, but creating systems with complex behavior is apparently not that difficult. The researchers in the article, alone, have demonstrated relatively complex behavior arising out of relatively basic approximations of nature's biological "small-world" communication networks; not to mention researchers are finding, within countless scientific fields, the propensity of all systems towards pattern formation, replication, and evolution (i.e. order from apparent chaos). The point being: as a "model" of a human brain becomes ever more functionally-equivalent in both structure and order of complexity to that of the reality of human brain biology (such as may be possible with tomorrow's supercomputers or quantum computers), that "model" will become increasingly indistinguishable, in its outward behavior, from that of a "real" brain. At what point does the model become the reality?
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Unfortunately, the article failed to address the significant impact quantum mechanical properties of nature must have on the behavior of neurons and the patterns of activity that result. The capacity for logical and conceptual thinking in humans are indicative of "small-world" neural networks ("brains") in which the quantum probabilistic effects collectively imparted on network information nodes ("neurons") provide the probabilistic "mental space" in which sophisticated simulations of hypothetical circumstances can be run and analyzed for statistically-meaningful patterns in order to find the course of action deemed most likely to result in--whatever is for "it"--the most favorable outcome. "Creativity", "intent", and "planning" are commonly recognized features of intelligence that stem from this ability. It could be argued that the degree of intelligence of any system is inherently proportional to the level of sophistication of a system's simulatory capacity, and thus, its capacity to correctly predict outcomes in order to more efficiently and effectively adapt to changing conditions.
Reply | Report Abuse | Link to this...
Even more unfortunate, our understanding of consciousness and its reflection, reality, have been impeded for centuries by a wrong turn the majority of the physics and philosophy community took in blindly accepting rigid, popular, "Western", linear ideas about the nature of "space" and "time". These theories provided conceptual convenience and attractive simplicity, but at the cost of severally distracting the overall academic community away from consideration of equally-plausible alternative interpretations of observed phenomenon that could potentially have already provided for compatibility with quantum mechanics. Many of the aspects of Newtonian and "Einsteinian" physics are certainly still relevant because they provide a large part of our observational inventory from which to begin pattern analysis. However, any theory that is founded upon even a single assumption, such as a "3-directional" cartesian "space" or the "curvature" of a "space" that isn't even explicitly defined by the same theory proposing the "curvature", can not be the underlying truth we seek. Keep in mind also that outside the human conceptual world, there are no perfectly straight lines, or perfect right angles, or perfectly round spheres (not even black-holes, since they all rotate to at least some degree), nor even a perfectly linear passage of time; there are only "atomized", probabilistic approximations. Indeed, the scientific community has frequently acknowledged over the last century that classical and relativistic physics are incomplete interpretations of reality. The fact that those theories were built on top of faulty, over-simplified geometric interpretations and therefore written in the wrong mathematical language explains their incompatibility with each other and quantum mechanics. Relations between material objects must be expressed in a language compatible with the way in which objects in the real world actually interact--through the transmission/reception of information. Perhaps a re-envisioning of classical and relativistic observations as being nothing more than the macroscopic overlap of quantum effects, combined with a calculus-integrated polar-vector/-field approach to the underlying geometry, can provide a promising intellectual lead towards understanding the deepest mysteries of the mind itself.
Reply | Report Abuse | Link to thisThe blurb under the title said "The noise of billions of brain cells trying to communicate with one another may hold a crucial clue to understanding consciousness". I read the article eagerly as new clues to understanding consciousness are thin on the ground; but I found nothing about this. What is the crucial clue?
Reply | Report Abuse | Link to thisThe brain evolved to ensure that its host and off-spring survived and vice versa. Transferring the structure to an inanimate structure would require also retraining to the novel function and hardware it should fight to maintain. It would otherwise miss(pine for) Norwegian fjords or Roquefort with a glass of good Bordeaux wine and a sweet kiss which would all be without import. Its future would be infinite yet invariable. Hopes of a better future could be incorporated if it could help plan a new generation of improved 'beings', perhaps searching for other partners with similar aims...and would it dream of electric sheep?
Reply | Report Abuse | Link to thisOk, so that's a lot of synapses, but does the 'hundred trillion' connections include the feedback loops by dendrites?
Reply | Report Abuse | Link to this100 trillion connections sounds formidable to make sense of in language of any kind but there is a method that is not itself dependent on language and that delineates the meaning inherent in all language. It delineates the structural dynamics of how all phenomenal experience is organized and integrated such that all possible varieties of experience are encompassed. Although it is not possible to intuitively conceive of anything outside it, it is not a theory of everything. It is a universal methodology that both facilitates and requires direct intuitive insight into the structural dynamics of how things work. It is not a belief system. It requires direct confirmation in phenomenal experience consistent with the empirical evidence. It complements traditional approaches to neurophysiology and science in general. It is introduced at www.cosmic-mindreach.com with two advanced articles on the human nervous system that demonstrate how it is structured to meaningfully integrate human experience synapse by synapse. Have a look. You will find it interesting and challenging.
Reply | Report Abuse | Link to thisThe article notes that Rockmore and Pauls have begun to identify major brain nodes using fMRI. In addition, Sporns et al have found in their model that a shutdown of such a major node can cause the entire system to misfire. I wonder if repair of a hub that has been damaged in brain injury can result in rapid advancement in several other areas due to this cascading effect. I wonder if Rockmore and Pauls' investigations will identify important hubs which others can then find therapies and exercises to repair.
Reply | Report Abuse | Link to thisAn anecdotal example: a family member of mine had a brain injury which caused coma. During her recovery over the past few years there have been step-changes in her global functioning where a particular function seemed to reemerge following extensive cognitive exercise. Reemergence of this particular area was followed quickly by several other functions. I wonder if node interplay is at work in what we witnessed.
While I can see that maybe some neurons have thousands of connections, I find it improbable that this is the average. With 100 billion neurons having 100 trillion connections, that is an average of 2000 connections per neuron (if you don't count each connection twice). How did they determine this?
Reply | Report Abuse | Link to thisNeurons and transistors are both either on or off. If the brain has 100 billion neurons each with 1000 connections there are 100 trillion connections. Nerve conduction velocity is 200 miles per hour, though much slower at each connection, and the nerve frequency is about 1000 times a second. If an integrated circuit has 1 billion transistors each with say one connection there are one billion connections. The speed of light is about 670,615,200 miles per hour and the frequencies of the transistors are say a gigahertz or a billion times a second.
Reply | Report Abuse | Link to thisIn Scientific American Mind ~ article = mind-reviews-the-other-brain is a review* of R.Douglas Field's 2009 book about how new discoveries about glial cells and astrocytes are revolutionizing medicine and science. Fields has shown that this "Other Brain" has its own communication system and regulates synaptic activity.
Reply | Report Abuse | Link to thisIf scientists are hoping to cure ailments like dementia and schizophrenia then being neuro-chauvinistic is tending towards sloppy science.
(* the first paragraph of the review should be deleted as quite misleading and rather irrelevant).
The most important clue to the nature of brain activity and memory comes at the beginning of the article, almost unnoticed. This is the ability of a single neuron to spontaneously generate electricity. This means in essence that any group of neurons have the ability to create characteristically unique circuits in response to each unique stimulus. These circuits can last as long as needed, and as long as that group of cells remain alive. It must be those associative circuits that constitute the electrical component of memory. No two circuits will be identical, among 100 trillions possible pathways, but the dominant circuits will almost democratically stand out as our most dominant memories. I've always thought this,(ever since I learned of neurons,) but never had occasion to write it before.
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