用憶阻器模擬人腦學習過程
Memristor emulates neural learning
我們一般把人腦的位細胞叫做突觸(synapses)，美國密西根大學研究人員指出，憶阻器(memristor)的功能特性是所有的電子組件中與突觸最相近的；他們最近展示單一憶阻器如何以與人腦相同的方式來學習同樣的技術。
PORTLAND, Ore. — Synapses are the bit-cells of the brain, and they
behave more like memristors than any other electronic circuit element,
according to the University of Michigan researchers who recently
demonstrated that a single memristor can learn using the same technique
as the human brain.
人類的神經網絡的學習模式，能達到像是特殊算法那樣、對工程師來說都很難的程度，主要是依賴一種稱為突觸的模擬內存元素；該元素目前正被科學家用做今日超級計算機中的一種數值來進行模擬。學習行為的發生，是由於來自人體各感官的功能探測器──像是眼睛內的邊緣傳感器，就會產生一種同步電壓突波。當同步突波發生時，負責接收的大腦突觸會透過提高其數值(也就是一種超級計算機模擬中使用的數字)來回應，而憶阻器則是以改變其電阻值的方式來回應。
Neural networks can learn patterns that are too difficult
for engineers to craft as specific algorithms, but they depend on an
analog memory element called a synapse, which today is simulated on
supercomputers as a numerical value. Learning occurs when simultaneous
voltage spikes are generated from feature detectors in the senses, like
edge detectors in the eye. When the simultaneous spikes come in, say
from the edge detectors in both eyes, the receiving synapse in the
brain responds by increasing its value--a digit used for supercomputer
simulations. Instead, memristors change its resistance value.
研究人員示範以憶阻器來模擬人腦神經網絡的學習功能
Researchers
demonstrate memristors emulating the learning function of a neural
network by changing the strength of its synaptic connections in
response to synchronized voltage spikes.Their findings will be
published in Nano Letters.
該研究團隊領導人、密西根大學教授Wei Lu表示，憶阻器是以幾乎與大腦突觸相同的「STDP (spike timing
dependent
plasticity)」模式，來回應同步電壓脈衝，因此使其能夠成為超級計算機模擬的替代方案。由惠普實驗室研究人員所發表的大規模憶阻器縱橫閂網絡，
可望建立一個比超級計算機更精確、快速的人腦功能模擬。
According to the University of Michigan researchers led by
Professor Wei Lu, memristors respond to these simultaneous voltage
pulses--called spike timing dependent plasticity--in a manner nearly
identical to that of brain synapses, making them a viable alternative
to supercomputer simulations. Massive crossbar networks of memristors, proposed by HP Labs researchers could create a more accurate and much faster executing emulation of brain functions than supercomputer simulations.
去年，美國國防部高等研究計劃局(Darpa)指派三個分別由HP、IBM與HRL Labs所率領的研究團隊，在其SyNAPSE計劃下，去研究出一種開發人腦學習要素的最佳方法；而研究成果將在明年發表原型。
Last
year, the Defense Advanced Research Project Agency (Darpa) signed up
three teams led by HP, IBM and HRL Labs to determine the best way to
develop the brain's learning element in its SyNAPSE (Systems of
Neuromorphic Adaptive Plastic Scalable Electronics) program. A
prototype is due by next year.
HP已經在上述計劃下，研究採用憶阻器來做為突觸，其成果將在今年稍晚發表。IBM去年也宣佈，已成功利用精確的超級計算機模擬貓腦；該獲獎的超級計算機算法名為「Blue Matter」最終將轉到硬體端，利用諸如密西根大學所研發的電子突觸來運行。
Hewlett Packard has been studying the use of memristors as
synapses for the Darpa program, and will be describing its efforts
later this year. Last year IBM announced it has achieved an accurate
supercomputer simulation of a cat brain, for which it received the
Association for Computing Machinery Gordon Bell Prize
at Supercomputer 2009. Called Blue Matter, the simulation could
eventually be transferred to hardware using electronic synapses like
those being developed at University of Michigan.
「貓腦的模擬是一個較現實可行的目標，因為其功能比人腦要簡單許多；但要仿製其複雜性與效益，仍然是非常困難的工作。」Lu表示；其研究團隊的目標是製作出某一天能在性能上媲美超級計算機的憶阻器設備，但機器的外型尺寸僅有兩公升的汽水寶特瓶那麼大。
"The cat brain sets a realistic goal because it is much simpler than a human brain, but still extremely difficult to replicate in complexity and efficiency," said Lu. The goal would be to create memristive devices that someday achieve the performance of a supercomputer in a machine the size of a 2-liter bottle.
The University of Michigan research was funded by both Darpa and the National Science Foundation.
留言列表