Slashdot Mirror
Our Brains Use Binary Logic, Say Neuroscientists (sciencedaily.com)
"The brain's basic computational algorithm is organized by power-of-two-based logic," reports Sci-News, citing a neuroscientist at Augusta University's Medical College.
hackingbear writes:
He and his colleagues from the U.S. and China have documented the algorithm at work in seven different brain regions involved with basics like food and fear in mice and hamsters. "Intelligence is really about dealing with uncertainty and infinite possibilities," he said. "It appears to be enabled when a group of similar neurons form a variety of cliques to handle each basic like recognizing food, shelter, friends and foes. Groups of cliques then cluster into functional connectivity motifs to handle every possibility in each of these basics. The more complex the thought, the more cliques join in."
2^11
It's pretty much obvious at first glance.
In a world of the blind, the one-eyed man is king--and the two-eyed man is a heretic.
I only glanced at the news article summary of the research, but it seems like they're saying you need an exponential number of neuron groups compared to the amount of information in bits that is coming in or being classified. It's hard to see how ONLY this organizing principle could be efficient in terms of amount of matter and energy needed to store the info.
There must be another organizing principles going along with this, to make it representationally and physically efficient.
Principles such as perhaps:
1) The cliques are organized in an efficient representation abstraction hierarchy
2) Only create neuron cliques for those combinations of things which DO occur, rather than the exponential number of possible combinations which do not occur in the world. i.e a sparse distributed representation, as has been suggested in other neuroscience and computational neuroscience research.
Where are we going and why are we in a handbasket?
Yes
I mean, maybe it's just bad reporting, but seriously:
At the heart of the Theory of Connectivity is the algorithm, N=2i–1, which defines how many cliques are needed for an FCM and which enabled the authors to predict the number of cliques needed to recognize food options, for example, in their testing of the theory.
“N is the number of neural cliques connected in different possible ways; 2 means the neurons in those cliques are receiving the input or not; i is the information they are receiving; and -1 is just part of the math that enables you to account for all possibilities,” Dr. Tsien said.
First off, that's not an algorithm but an equation. i is the information they're receiving? But in context, i is a number. Finally, "the part of the math that enables you to account for all possibilities"? what does that even mean?
There are 10 types of people: those who understand number systems, and 10-1 of those who don't realize that every base is base 10 in base 10.
Ezekiel 23:20
*golf clap*
You can't spell "oneiromancy" without "roman".
This seems to say that by interlinking subsets of binary decision modules you can simulate (or create?) a silicon based system that will approximate the decision tree of a biological entity. Toss in an additive memory system that enhances pattern recognition based on past experience (machine learning compatible?) and you have a system that will grow in discernment the way biological systems do. The trick would seem to be in modeling the appropriate type and number of these underlying modules, designing them to revise their output based on the relevant memory experience, and then assigning priority to the outputs from those modules, giving self preservation and threat detection precedence for instance.
(Half cocked speculation) I can see custom cores designed to evaluate input based on their own narrow realm of specialization (food, friend/foe, threat/non-threat, shelter, etc. could be analogous to other machine relevant inputs) and with their own memory stores of experiential reference material. These feeder cores would process input with regard to their own specialization and then hand off their individual result to another coordinating core designed to integrate results from the feeder cores. The coordinating core would have a prioritization system to weight the inputs and handle conflicts. The coordinating core would also build an experiential database comprised of inputs from the other core modules and the results of the decisions made from those inputs and the viability of the decisions.
Emergent phenomena and complexity would seem to be a logical result of the combination of a large array of interacting modules provided the output space is varied and robust.
When the only tool you have is a claw hammer every problem starts to look like the back of someone's skull.
FileNotFound LOL!
"It's a good computer... for I to BM on!" - apologies to Triumph, the insult comic dog
I've been waiting for a good opportunity to take this new toy out for a spin. Semantic Scholar claims to have brain science almost completely covered.
* author search
Not bad.
* topic search
Not blindingly great. But the third link down is a primary hit.
Theory of Connectivity: Nature and Nurture of Cell Assemblies and Cognitive Computation
There's not a lot of related material here that I'd have gone chasing after the hard way. Apparently, either this research result or this search engine is still too new.
Nevertheless, I retain high hopes.
They must mean eight (numbered 0-7, of course).
Summary of the TFA : the actual method of brain signaling is primarily all or nothing electrical impulses. The timing is analog - THEORETICALLY a difference in an electrical impulse arriving down to the planck-second could have an effect.
This research does not change any of this. The brain is still analog at the individual synapse level, it just follows certain patterns that are related to binary math for setting up arrays of neural circuitry.
In practice, like any analog system, true resolution is finite because there is noise. So the system merely needs to be quantized down to the level of resolution of noise and you can replicate it's behavior exactly in a digital equivalent. Remember analog PIDs and other simple analog computers, the ones that used vacuum tubes and were used from ww2 and a few decades after? Those systems also had finite effective resolution even though analog systems theoretically have infinite resolution. That was because of all the various forms of noise in the actual physical equipment. In practice if you replace an analog system with a digital system you can get BETTER resolution because all the intermediate processing steps do not introduce additional noise. (while each vacuum tube op amp you solder in picks up extra noise that is added to the signal)
Kind of the first joke I thought of too; but... what if our desire to ask such questions is the result of some sort of built-in bias? What if truth is N-dimensional and we just can't conceive of it in any real way? Note, not gradations of T and F, but an actual N-dimensionality of veracity. Like, I'm telling the truth, I'm telling a lie, or I'm telling an arrow.
For all intensive purposes, "whom" is no longer a word. That begs the question, "who cares"?
The most shocking part of this study is that the human brain uses logic. The authors thought they could slip that through? Who's going to believe it?
Neural network can produce logic or lossy yet meaningful metrics and then you can yet combine multiple levels and have precognitive assumptions and or forceful cognitive assumptions. Result: deterministic complexity vs statefull and relevant guesses.
Static logic is best left to assembly code and extrapolated guesses to trained neural matrices. Repeat after me... left, right.. left, right... left, right...
Of course the world is full of two bit people hen decreasing in number 4bit , 8 bit 16,32,64 all the way out to 512 bit and beyond of in color terms those that see in black and white to those that can see shades of grey and and the whole color spectrum but like color beyond 32 bit it gets hard to tell the difference
Your'e all thinking it, I just said it for you
Being saying this for years. If our braun behaviour is a consequence of neurin behaviour, this is inevitable. Think of a horde of Minions, each with a different idea of what a banana looks like, and a single button each to press if they see what they think is banana.
John_Chalisque
Sure we have binary circuits in the brain but logic is another issue completely. Humans will have all kinds of things going on that do not relate to the task or subject at hand. For example, looking good, retaining a posture of self, or showing that you can act up at times, may take precedence over accomplishing the goal at hand. Logic is not really a human characteristic.
The world is analog. Digital is just a high gain approximation. In nueral networks we spend huge computational resources to approximate digitally the stochastic processes at work a single nueron. The brain isn't even close to being digital.
It's just Buzzfeed with a different web design and the same old trolls.
Mike @ The Geek Pub. Let's Make Stuff!
Simply having a larger word size isn't what I'm talking about though. Larger word sizes are just a composition of binary bits. I'm thinking more like the Flatland analogy. We know what true and false are, but we don't know what "arrow" is.
In Flatland, a sphere projects onto the plane and is perceived as a circle. In our world, an "arrow" veracity might project as a paradox. For example, "everything I'm telling you is a lie" is a classic statement that defies analysis as "true" or "false". Perhaps it's an "arrow" statement projected onto our world.
For all intensive purposes, "whom" is no longer a word. That begs the question, "who cares"?
& those who think ternary jokes are better.
(Puts dark glasses on.) The brain is digital. The brain is made of atoms and atoms are digital, look closely enough and everything is digital. Of course that is pedantic nonsense but that is how I'm feeling today. :) (because the number of atoms in the brain changes constantly and atoms are below our point of consideration - ie in most circumstances they effectively don't exist as individual objects.)
(More sensibly) Anyone who reads even the basics of neural signalling will tell you that brains use hybrid digital analog logic. Neurons use pcm coding which transmits both digital and analogue signals using variable timing in trains of pulses.. It is pretty bizarre if they use binary arithmetic as well, it sounds like the article is not talking about arithmetic directly though but communications logic - which is something totally different.
Below the speed of light Special Relativity is one of the most accurate theories in physics - above the speed of light..