brain - York Jong on Diigo

29 Nov 09

Neuron (software) - Wikipedia, the free encyclopedia

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It was primarily developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke.

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neuron brain

11 Jun 09

淺眠多夢…能有好點子

據說有一些陷入苦思的大科學家往往在一夜好眠後，發現問題的解答或重要理論。所以，當我們碰到百思不解的難題時，是不是先睡個大頭覺，或許有助於問題的解決？加州大學聖地牙哥分校的研究發現，多夢的睡眠確實有提升創意的效果。

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brain sleep

多夢的睡眠確實有提升創意的效果

31 May 09

人為何生而為人？

人類和黑猩猩的基因組只有些微不同，但是科學家在其中找到了人類獨有的DNA片段，這些片段在腦、手與其他部位作用，讓人類與黑猩猩大不相同。

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biology brain DNA

在600萬年前人類和黑猩猩的譜系分開後，人類基因組的30億個核酸中，僅有約1%，也就是約1500萬個核酸鹼基改變了
基因組中有些改變較快的區域，是受到了正向天擇的記號，那些突變有利於生物的生存和繁衍，因此更有可能傳給後代。換句話說，在人類和黑猩猩分開後變化最多的編碼，就是最有可能塑造我們成為人類的序列
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- HAR1在前幾億年的期間沒有改變，代表它具有非常重要的功能，而在人類基因組中的快速變化，則暗示著它在人類的功能可能有重大修改
- 螢光標示實驗顯示，HAR1會在某一類的神經元中活化，而這種神經元會影響大腦皮層（大腦最外層的皺摺構造）發育時的模式和架構
- HAR1另一個特點是它並不會轉譯出蛋白質
- 我們從HAR1序列的模式，推測這段序列會用來製造RNA分子
- HAR1也是已知第一個可能受到正向天擇的RNA編碼序列。
  之前沒有人注意過人類基因組上這驚人的118個鹼基

06 May 09

創智慧-理解人腦運作，打造智慧機器

智慧究竟是什麼？二十多年來人工智慧和神經網路等想要模擬人類智慧的嘗試失敗，正是因為他們沒有先了解什麼是智慧。霍金斯提出一個強有力的理論，解釋人類大腦是怎樣運作以及為什麼他認為電腦沒有智慧，深入探討如何建構出真正的智慧機器。

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book choice brain AI

發展的認知神經科學

一本比較深的認知神經科學書，作者希望能引導更多人進入認知神經科學的領域。除了說明先天表徵和結構規範是如何使得神經網路內的表徵出現，並介紹了認知神經科學發展上的重要研究方法，最後並提出對未來研究方向的建議。本書論述精闢，主要是探討腦部成長與心智歷程發展之間的關係。這個跨學門的領域將分子生物學、神經網路、腦部造影近年來的進展整合起來，因此它正在快速擴張中，也提供了人類心智結構一個整體的看法。

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book choice brain neuron development

腦力大躍進

科學家己經證實，大腦有如一塊超強的海綿，在出生到約12歲之間擁有超強吸收的！大腦越用會越好用，不用卻會生繡。而擁有聰明的腦子的好處還不只一樁--------人們從未想過，經常鍛鍊腦力還可以強身治病，甚至延年益壽！

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book choice brain learning

重塑大腦

兩位作者──傑佛瑞‧史瓦茲博士是腦部疾病的重要研究專家，夏倫‧貝格利是《華爾街日報》的科學專欄作家──在書中指出心靈是獨立存在的，能夠形塑與控制大腦的運作。他們的研究一部份要歸功於科學界對成人大腦可塑性的新發現──科學家發現大腦的可塑性並非侷限於童年，而是延續一輩子。

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book choice brain quantum

本書為美國精神病學、神經科學、心理學教授史奎爾(Larry R. Squire)及二○○○年因研究海蝸牛機制而獲諾貝爾生醫獎得主肯戴爾(Eric R. Kandel)第一本有關記憶研究的革命性發現與看法，除了了解記憶是如何作用，並從分子生物學的領域中探究大腦的記憶系統與認知記憶儲存機制，從而尋求研發治療因年齡而造成記憶衰退的藥物，以及減緩阿茲海默症病情惡化的治療法，對於未來神經學和精神病學在醫學上的研究和臨床治療，將有莫大的幫助與影響力。

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book choice brain neuron memory

腦中有情─奧妙的理性與感性

本書並非在探討情緒心理層面與大腦功能間的應對關係，而是研究大腦如何從一個新的觀點來了解情緒，並藉由神經系統的內部去了解這些令人困惑的心智狀態。

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book choice brain neuron emotion

驚異的假說

驚異的假說：克里克的「心」、「視」界

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book choice brain neuron psyche

大腦如何思考

人類內在的心智活動到底怎麼由此刻狀態變成下一刻狀態？大腦究竟如何使我們擁有心智活動？並使我們擁有智能以面對未知？

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book choice brain

16 Mar 09

一個腦神經科學家中風後復原的《奇蹟》

你認為中風是怎樣的一個過程？你是不是也和我之前一樣，認為應該就是身體的某一部份突然不能動了？你認為中風後的人跟之前有什麼不同？你是不是也和我之前一樣，認為應該只是身體的某些部位變成不能動了？

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medicine brain

05 Feb 09

即使中風，也不是絕望的時候

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medicine brain

02 Jan 09

矽眼

The Silicon Eye: How a Silicon Valley Company Aims To Make All Current Computers, Cameras, and Cell Phones Obsolete

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book choice retina brain

24 Aug 08

有「腦」的機器人今日誕生

英國科研人員13日推出一個由老鼠的腦組織控制的機器人。法新社說，這可能是世界上首個完全由活體腦組織控制的機器人。科研人員認為，“鼠腦”機器人研究項目具有深遠的科研和醫學意義。 “戈登”其人：老鼠腦加機器身

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brain robot

科研人員將神經細胞從老鼠身上分離下來，放置在酶溶液中，讓這些神經細胞彼此分離。然後，他們將這些神經細胞置于營養豐富的培養基中。培養基容器長寬均為8厘米，與一個擁有60個電極的電子矩陣相連接
電子矩陣是活體腦組織和機器部件的接合面。通過電子矩陣，“戈登”大腦發出電子脈衝，驅動機器人的輪子，同時接受傳感器基于外部環境刺激發出的脈衝
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- “戈登”大腦是活的組織，因此必須裝在特定溫度控制的器具中。“戈登”大腦通過藍牙無線連接同自己的“身體”聯絡。除受自身大腦支配外，“戈登”不受額外的人為或電腦控制
- 老鼠大腦由大約100萬個神經細胞構成，人腦則包含大約1000億個神經細胞。沃裏克推測，老鼠和人智力上的差異應歸結為數量不同而非性質不同

22 Jun 08

海扁學習與神經網路的同步化

今天就先和各位分享神經網路調整的兩種方法：海扁學習和STDP，並且另外介紹神經網路同步化 (synchronized)和同多步 (polychrnous)的模型，進一步探討可能的神經網路模式，或許對人工智慧自我學習的方法上，也能提供一些參考！

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brain neuron learning

23 Feb 08

New Directions in Statistical Signal Processing

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book brain choice signal statistics

16 Sep 07

心靈機器時代 —當電腦超越人腦

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AI book brain choice computer

09 Sep 07

When will computer hardware match the human brain? by Hans Moravec

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5star AI MIPS brain computer megabytes memory trend

The processing power and memory
capacity necessary to match general intellectual performance
of the human brain are estimated. Based on extrapolation of
past trends and on examination of technologies under
development, it is predicted that the required hardware will
be available in cheap machines in the 2020s.
Programs need memory as well as processing speed to do
their work. The ratio of memory to speed has remained
constant during computing history.
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- Dividing memory
  by speed defines a "time constant," roughly how
  long it takes the computer to run once through its
  memory. One megabyte per MIPS gives one second, a nice
  human interval.
- Machines with less memory for their
  speed, typically new models, seem fast, but unnecessarily
  limited to small programs. Models with more memory for
  their speed, often ones reaching the end of their run,
  can handle larger programs, but unpleasantly slowly.
- Customers maintain the ratio by asking
  "would the next dollar be better spent on more speed
  or more memory?"
- The megabyte/MIPS ratio seems to hold
  for nervous systems too! The contingency is the other way
  around: computers are configured to interact at human
  time scales, and robots interacting with humans seem also
  to be best at that ratio.
- faster
  machines, for instance audio and video processors and
  controllers of high-performance aircraft, have many MIPS
  for each megabyte. Very slow machines, for instance
  time-lapse security cameras and automatic data libraries,
  store many megabytes for each of their MIPS.
- Flying
  insects seem to be a few times faster than humans, so may
  have more MIPS than megabytes. As in animals, cells in
  plants signal one other electrochemically and
  enzymatically. Some plant cells seem specialized for
  communication, though apparently not as extremely as
  animal neurons. One day we may find that plants remember
  much, but process it slowly
- With our conversions, a 100-MIPS robot, for instance
  Navlab, has mental power similar to a 100,000-neuron
  housefly. The following figure rates various entities.
- MIPS and Megabytes.
- Universal computers
  can imitate other entities at their location in the
  diagram, but the more specialized entities cannot. A
  100-million-MIPS computer may be programmed not only to
  think like a human, but also to imitate other
  similarly-sized computers. But humans cannot imitate
  100-million-MIPS computers--our general-purpose
  calculation ability is under a millionth of a MIPS.
- Computers doubled in capacity every two years after the war, a
  pace that became an industry given: companies that wished to grow
  sought to exceed it, companies that failed to keep up lost
  business. In the 1980s the doubling time contracted to 18 months,
  and computer performance in the late 1990s seems to be doubling
  every 12 months.
- Faster than Exponential Growth in
  Computing Power. The number of MIPS in $1000 of
  computer from 1900 to the present. Steady improvements in
  mechanical and electromechanical calculators before World War II
  had increased the speed of calculation a thousandfold over manual
  methods from 1900 to 1940. The pace quickened with the appearance
  of electronic computers during the war, and 1940 to 1980 saw a
  millionfold increase. The pace has been even quicker since then,
  a pace which would make humanlike robots possible before the
  middle of the next century. The vertical scale is logarithmic,
  the major divisions represent thousandfold increases in computer
  performance. Exponential growth would show as a straight line,
  the upward curve indicates faster than exponential growth, or,
  equivalently, an accelerating rate of innovation. The reduced
  spread of the data in the 1990s is probably the result of
  intensified competition: underperforming machines are more
  rapidly squeezed out. The numerical data for this power curve are
  presented in the appendix.
- Chip progress not only continued, it
  sped up. Shorter-wavelength light was substituted, a more precise
  way of implanting impurities was devised, voltages were reduced,
  better insulators, shielding designs, more efficient transistor
  designs, better heat sinks, denser pin patterns and
  non-radioactive packaging materials were found. Where there is
  sufficient financial incentive, there is a way. In fact,
  solutions had been waiting in research labs for years, barely
  noticed by the engineers in the field, who were perfecting
  established processes, and worrying in print as those ran out of
  steam. As the need became acute, enormous resources were
  redirected to draft laboratory possibilities into production
  realities.
- The wave-like nature of matter at very small scales is a problem
  for conventional transistors, which depend on the smooth flow of
  masses of electrons. But, it is a property exploited by a radical
  new class of components known as single-electron transistors and
  quantum dots, which work by the interference of electron waves.
  These new devices work better as they grow smaller. At the scale
  of today's circuits, the interference patterns are so fine that
  it takes only a little heat energy to bump electrons from crest
  to crest, scrambling their operation. Thus, these circuits have
  been demonstrated mostly at a few degrees above absolute zero.
  But, as the devices are reduced, the interference patterns widen,
  and it takes ever larger energy to disrupt them. Scaled to about
  0.01 micrometers, quantum interference switching works at room
  temperature.
- The big freeze. From
  1960 to 1990 the cost of computers used in AI research declined,
  as their numbers dilution absorbed computer-efficiency gains
  during the period, and the power available to individual AI
  programs remained almost unchanged at 1 MIPS, barely insect
  power. AI computer cost bottomed in 1990, and since then power
  has doubled yearly, to several hundred MIPS by 1998. The major
  visible exception is computer chess (shown by a progression of
  knights), whose prestige lured the resources of major computer
  companies and the talents of programmers and machine designers.
  Exceptions also exist in less public competitions, like petroleum
  exploration and intelligence gathering, whose high return on
  investment gave them regular access to the largest computers.
- Agony to ecstasy. In
  forty years, computer chess progressed from the lowest depth to
  the highest peak of human chess performance. It took a handful of
  good ideas, culled by trial and error from a larger number of
  possibilities, an accumulation of previously evaluated game
  openings and endings, good adjustment of position scores, and
  especially a ten-million-fold increase in the number of
  alternative move sequences the machines can explore. Note that
  chess machines reached world champion performance as their
  (specialized) processing power reached about 1/30 human, by our
  brain to computer measure. Since it is plausible that Garry
  Kasparov (but hardly anyone else) can apply his brainpower to the
  problems of chess with an efficiency of 1/30, the result supports
  that retina-based extrapolation. In coming decades, as
  general-purpose computer power grows beyond Deep Blue's
  specialized strength, machines will begin to match humans in more
  common skills.

12 Jul 07

同理心可量測台醫師破解自閉症密碼

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brain news

York Jong's Library tagged brain → View Popular

Neuron (software) - Wikipedia, the free encyclopedia

淺眠多夢…能有好點子

人為何生而為人？

創智慧-理解人腦運作，打造智慧機器

發展的認知神經科學

腦力大躍進

重塑大腦

透視記憶

腦中有情─奧妙的理性與感性

驚異的假說

大腦如何思考

一個腦神經科學家中風後復原的《奇蹟》

即使中風，也不是絕望的時候

矽眼

有「腦」的機器人今日誕生

海扁學習與神經網路的同步化

New Directions in Statistical Signal Processing

心靈機器時代 —當電腦超越人腦

When will computer hardware match the human brain? by Hans Moravec

同理心可量測台醫師破解自閉症密碼

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