神經(jīng)信息工程研究前沿

內(nèi)容概要

　　《國際科技發(fā)展前沿叢書：神經(jīng)信息工程研究前沿》從“腦機(jī)接口及臨床應(yīng)用”、“認(rèn)知計(jì)算與控制”、“神經(jīng)信息獲取、檢測與處理”、“神經(jīng)教育信息工程”和“運(yùn)動(dòng)假體神經(jīng)自主控制”等五個(gè)專題闡述重要科學(xué)問題，探討關(guān)鍵技術(shù)，總結(jié)研究成果，闡述當(dāng)前熱點(diǎn)，展望未來趨勢：分享本次會(huì)議的成果，相信對廣大科技人員和科研決策者具有現(xiàn)實(shí)的參考價(jià)值，期望能促進(jìn)同行的交流，進(jìn)一步推動(dòng)該領(lǐng)域的發(fā)展。

書籍目錄

神經(jīng)信息工程前沿現(xiàn)狀與展望——2011神經(jīng)信息工程研究前沿國際研討會(huì)綜述 一、特邀報(bào)告 利用意念控制機(jī)器——神經(jīng)工程技術(shù)在運(yùn)動(dòng)功能重建和腦傷治療方面的未來趨勢 心—腦科學(xué)研究前沿——神經(jīng)工程 二、腦機(jī)接口及其臨床應(yīng)用專題 運(yùn)動(dòng)學(xué)習(xí)和功能康復(fù)中腦皮層神經(jīng)活動(dòng)的適應(yīng)性 浙江大學(xué)求是高等研究院腦機(jī)接口研究進(jìn)展 植入式神經(jīng)接口和微型生物傳感器的研究 人腦單神經(jīng)元信號對外部設(shè)備的意念控制 三、認(rèn)知計(jì)算與控制專題 Cognitive Computation：The Ersatz Brain Project 智能車輛的視覺認(rèn)知計(jì)算 四、神經(jīng)信息獲取、檢測與處理專題 通往認(rèn)知腦機(jī)接口 連接大腦的神經(jīng)技術(shù)：挑戰(zhàn)與機(jī)遇 光學(xué)腦功能成像研究和成果轉(zhuǎn)化 穩(wěn)態(tài)視覺誘發(fā)電位在腦機(jī)接口中的應(yīng)用 五、教育神經(jīng)信息工程專題 教育神經(jīng)工程對計(jì)算智能的需求 神經(jīng)教育工程：教育發(fā)展的新時(shí)代 教育神經(jīng)工程中的社會(huì)情緒能力評價(jià) 六、神經(jīng)控制與運(yùn)動(dòng)修復(fù)專題 智能機(jī)械手Smart Hand的設(shè)計(jì)及實(shí)驗(yàn)評估 繞過損傷的脊髓：用皮層控制的功能電刺激實(shí)現(xiàn)上肢的抓取功能 多功能上肢假肢的仿生控制 基于面部運(yùn)動(dòng)區(qū)和Wernicke區(qū)皮層微電極陣列信號的單詞發(fā)音分類 七、青年學(xué)者專題 mGRASP技術(shù)“繪制”大腦環(huán)路 靈巧假肢設(shè)備的神經(jīng)控制 光基因技術(shù)靜息位置細(xì)胞活動(dòng)中的PV中間神經(jīng)元 面向神經(jīng)假體和神經(jīng)機(jī)器人的大腦微刺激 基于近紅外功能成像技術(shù)的腦機(jī)接口 神經(jīng)集群活動(dòng)的低維表征

章節(jié)摘錄

版權(quán)頁：   插圖：   Brain "hardware" is glacially slow in comparison.The basic nerve cells-neurons-rarely operate faster than 1,000 times a second,a million times slower than a silicon CPU.There are a whole series of essential biological mechanisms that make nerve cells noisy.Nerve cells are affected by many malign influences,from bad biochemicals,to mechanical shock,to viruses and bacteria.But brain hardware works in a continuous world,that is,instead of only one's and zeros,neurons can signal all the values between zero and their fastest response rate.The cerebral cortex processes information in huge chunks.Instead of 64 bits at a time,ten billion nerve cells can be working on the same problem at the same time. The hardware is so different that it is surprising that anyone ever thought they worked in the same way,but a lot of smart people did.The term "Artificial Intelligence" [AI] was first used at a famous summer long gathering at Dartmouth in 1956.Most of those who thought about the problem of smart machines were there for at least part of the summer.Their goal was to mimic human intelligence with a machine: "AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable,within twenty years,of doing any work a man can do" and Marvin Minsky agreed,writing that "within a generation… the problem of creating 'artificial intelligence'will substantially be solved" Alas,such was not to be. There was a consensus at that time that intelligent systems were forced by some unspecified law of nature to follow common universal rules of reasoning,and thinking.As a convenient consequence,if you understood machine intelligence well enough,you didn't have to spend time learning the details of human intelligence because they were the same.Since the Dartmouth participants were mathematicians,philosophers,computer scientists and engineers,they assumed intelligence in its general form worked how they thought,or,more accurately,how they thought they thought.

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《神經(jīng)信息工程研究前沿》分享本次會(huì)議的成果，相信對廣大科技人員和科研決策者具有現(xiàn)實(shí)的參考價(jià)值，期望能促進(jìn)同行的交流，進(jìn)一步推動(dòng)該領(lǐng)域的發(fā)展。

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