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Computing Architectures

Computing Architectures. The human brain as computing system. Based on presentation from http://www.stanford.edu/class/symbsys100/ and http://www.willamette.edu/~gorr/classes/cs449/brain.html. Plan. From symbols to meat Meet the brain Brains vs. digital computers

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Computing Architectures

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  1. Computing Architectures The human brain as computing system Based on presentation from http://www.stanford.edu/class/symbsys100/ and http://www.willamette.edu/~gorr/classes/cs449/brain.html

  2. Plan • From symbols to meat • Meet the brain • Brains vs. digital computers • Bio-inspired computers • Reasoning module: concluding discussion

  3. Motto • Human cognition is based on a very specific computing system, with specific limits, inherent trade-offs, etc. that are not necessarily the same as for digital computers • It is therefore worth looking at the "mind's implementation" in order to learn more about the limits of our mind/cognition

  4. Plan • From symbols to meat • Meet the brain • Brains vs. digital computers • Bio-inspired computers • Reasoning module: concluding discussion

  5. The brain – just 2 pounds of meat? • The cortex • 1.3-1.4kg (2% of the body weight) … [13,14] • 2,500 cm2 (rat: 6 cm2, elephant: 6,300 cm2)[14] • 1,300-1,500 cm3 • 2 hemispheres connected by corpus callossum (250 mill. nerve fibers) • Inputs: • spinal cord • optic nerve (1.2 mill.) • cranial nerves (12) • auditory system, …

  6. The lobes • 4 lobes: occipital, parietal, temporal, frontal • Occipital: vision • Parietal: touch, pressure, temperature, pain • Temporal: auditory information, long term memory • Frontal: short term memory, planning, emotion, movement… • Biggest difference from our closest evolutionary ancestors

  7. Taken from http://www.sciencebob.com/lab/bodyzone/brain.html

  8. Neurons [14] • 100 billion neurons (children) • 300 million – octapus; • 18,000 – sea slug Aplysia; 350 - leech • Diameter: 4 – 100 microns • Weight: 10-6 grams • Length: <1 mm – 4 feet (in the leg) [15] • Length of Giraffe primary afferent axon: 15 feet • Loss of neurons: ~1/sec  31 million/year  an octapus/10 years • ~5,400 at the end of this lecture (sorry!)

  9. How do we know? • Non-invasive (1mm3 ~ 6-7*104 neurons) • EEG (Electroencephalogram), • ERP (Early receptor potential) • fMRI (blood flow; ~1mm; secs-mins) • MEG (Magnetoencephalogram with ERP: ~1.5mm; msecs-secs) • PET (imaging techniqueblood flow; 1mm; >mins) • Invasive methods: electrodes (1 neuron; msecs) • Lesions • Permanent: injury, disease • Temporary: specific drugs, TMS (<1mm; <secs) • All methods have trade-offs (spatial, temporal resolution)

  10. The brain as a computational system • The brain is • biological • de-central (plasticity) • non-digital • highly parallel • What does this mean?

  11. The brain: a biological CS • not manufactured from scratch with a certain intention in mind, but subject to evolution • Co-adaptation; its parts must have been of use • Not made out of copper or light-conduction cables ....  slow • Signal speed: MAX=120m/s, AV.=6.5m/sec (1.2 - 250mph) [14] • Signal frequency: up to 1000Hz (activ./sec)

  12. Non-digital • At least to some degree, the brain is non-digital • On the lowest level (i.e. within the neuron): quasi-digital • this creates an analog signal travelling along the neuron • at the synapses this is converted into a chemical signal, which in turn triggers an elecrical signal.

  13. The brain: a highly parallel CS • Some neurons have up to 150,000 connections (others as low as 2) • average: 1,000-10,000 [14] • different brain regions are highly interconnected • human can manage many tasks at the same time (sitting, listening to the lecture, doodling) • however, there are also parts of the brain which are involved in a lot of tasks  "narrow passages" for computation

  14. Plan • From symbols to meat • Meet the brain • Brains vs. digital computers • Bio-inspired computers • Reasoning module: concluding discussion

  15. Storage capacity of the brain - I • 100 billion neurons • 1011! hypothetically possible connections • upto 150,000 connections between each neuron (180,000km of myelinated nerves) • during the first year of life, the child generates ~ 15,000 connections for each neuron (during growth: 250,000 per minute!) • “… this program will support more than 130,000 [i.e. 1.3 * 105] neural connections…”

  16. Storage capacity of the brain - II • # bits = # of neurons * # of connections • 1 * 1011 * 1.5 * 105 = 1.5 * 1016 bits • The entire Enc. Britannica contains 109 bits of information (Turing 1950) • In 1987, Hideaki Tomoyori memorized the first 40,000 digits of π

  17. Information processing speed of the Brain • # bits/sec = # ops/sec* # bits/op • 10 ops/sec per synapse (connection) [3,4] • ~1.5 * 1017 bits/sec information transfer • Estimates of the brain's computing power range from 1011 to 1020 bits/sec • Converging evidence for ~ 1015 [2,3,5,9,14] • ~100 teraflops (8 bit words); ~ 8 teraflops (128 bits words)

  18. Brain vs. digital Computers • Fastest computer atm: • 40 terra flops(5,000 processors; NEC) • Planned • 360 terra flops (130,000 processors; IBM) • ~ 3-4 times faster than the human brain (8 bit words); 40 times faster otherwise.

  19. Plan • From symbols to meat • Meet the brain • Brains vs. digital computers • Bio-inspired computers • Reasoning module: concluding discussion

  20. Bio-inspired models of computation • This gives us a motivation to investigate bio-inspired models of computation • Learn about the brain by modeling it • Take advantage of billions of years of evolutionary design • Develop robust computational systems • Neural networks

  21. So what? • “It is true that a discrete-state machine must be different from a continuous machine. But if we adhere to the conditions of the imitation game, the interrogator will not be able to take any advantage of this difference.” Turing (1950:451)

  22. References • Gazzaniga, Ivry & Mangun (1998): Cognitive Neuroscience. The Biology of the Mind. Norton. • Merkle, Ralph C. (1988): How many bytes in human memory? at http://www.merkle.com/humanMemory.html • Merkle, Ralph C. (1989): Energy Limits to the Computational Power of the Human Brain; at http://www.merkle.com/brainLimits.html • Principles of Neural Science, by Eric R. Kandel and James H. Schwartz, 2nd edition, Elsevier, 1985 • http://www.coping.org/earlyin/ruleout/reason.htm • http://www.jsmf.org/zarticles&pap/John/neural_connections.htm • http://ifcsun1.ifisiol.unam.mx/Brain/neuron.htm • http://ifcsun1.ifisiol.unam.mx/Brain/neuron2.htm • http://www.rfreitas.com/Nano/DeusExDigita.htm • http://www.cheshireeng.com/Neuralyst/nrlnds.htm • http://www.top500.org/ • http://www.consciousness.arizona.edu/hameroff/ • http://www.neurologicalalliance.org.uk/pages/network/answers.asp • http://faculty.washington.edu/chudler/facts.html • http://www.uncc.edu/sspauldi/LECNote/ch02.html

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