德國(guó)圖賓根大學(xué)和馬克思·普朗克生物控制學(xué)研究所等多家單位開(kāi)展合作研究,，揭示了在決策過(guò)程中,，單個(gè)神經(jīng)元在保持與其他神經(jīng)元互相關(guān)聯(lián)的條件下是怎樣重建權(quán)重的。相關(guān)論文發(fā)表在最近出版的《自然·神經(jīng)科學(xué)》雜志上。
無(wú)論在社會(huì)生活中還是在自然界，制定決策通常都是諸多因素之間互相作用的結(jié)果。人們?cè)谧鲎罱K決定時(shí),，通常很難確定各種因素該占多大權(quán)重，即各種因素對(duì)于整個(gè)事件的重要程度,。神經(jīng)科學(xué)家也面臨著類似問(wèn)題,，因?yàn)闆Q定總是要由大腦神經(jīng)元來(lái)做。
比如,，當(dāng)我們看到對(duì)面街上某個(gè)人很像是自己的老朋友,，這一信息會(huì)通過(guò)大量神經(jīng)元輸入大腦。但在這些神經(jīng)元中,，哪些神經(jīng)元是把信息傳輸?shù)礁呒?jí)腦區(qū)的關(guān)鍵,？哪些神經(jīng)元決定了這個(gè)人是誰(shuí)？哪些決定了我們是否過(guò)去跟他招手問(wèn)好,？圖賓根大學(xué)沃納·雷查德綜合神經(jīng)科學(xué)中心教授馬蒂亞·貝斯杰領(lǐng)導(dǎo)的研究小組開(kāi)發(fā)出一種方程,，讓人們能算出一個(gè)感知神經(jīng)元在決策過(guò)程中的重要程度。
研究人員解釋說(shuō),，就像一個(gè)人如果獲得了與犯罪有關(guān)的內(nèi)幕消息,，他就會(huì)被認(rèn)為是可疑的，如果某個(gè)感知神經(jīng)元的活動(dòng)包含了與最終決策有關(guān)的信息,，也會(huì)被認(rèn)為在決策中起到某種作用,，一個(gè)神經(jīng)元就像是一個(gè)人。按照這種方式,，決策問(wèn)題就變成了神經(jīng)元之間不斷的通訊,。一個(gè)與決策無(wú)關(guān)的神經(jīng)元，可能只是簡(jiǎn)單地從鄰居那里接受信息,，而“加入”到會(huì)談中,。事實(shí)上,，鄰居神經(jīng)元發(fā)送的關(guān)鍵信號(hào)被傳給了大腦的高級(jí)決策區(qū),。
研究人員對(duì)這一過(guò)程進(jìn)行分析處理,，不僅考慮了每個(gè)神經(jīng)元的活動(dòng)中所包含的信息，而且考慮了神經(jīng)元之間的通訊,，并由此開(kāi)發(fā)出一種新方程,，能確定神經(jīng)元的讀取權(quán)重是怎樣獲得的，即解碼策略,，并確定是少量神經(jīng)元攜帶了大量有關(guān)決策的信息,，還是在大量互相連接的神經(jīng)元中包含了這些信息。
研究人員指出,，他們還開(kāi)發(fā)出一種測(cè)試方法,，在不知道某個(gè)神經(jīng)元與其他神經(jīng)元有何關(guān)聯(lián)的情況下，能確定其解碼權(quán)重對(duì)于一項(xiàng)任務(wù)而言是否最優(yōu),，這讓人們有可能分析一些更基本的問(wèn)題,，即在大腦利用信息做出的決策中，哪條決策路徑是最優(yōu)的,，哪條路徑次之,。(生物谷Bioon.com)
doi:10.1038/nn.3309
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PMID:
Inferring decoding strategies from choice probabilities in the presence of correlated variability
Ralf M Haefner, Sebastian Gerwinn, Jakob H Macke & Matthias Bethge
The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship that is often quantified by choice probabilities. Although choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. We derive the mathematical relationship between choice probabilities, read-out weights and correlated variability in the standard neural decision-making model. Our solution allowed us to prove and generalize earlier observations on the basis of numerical simulations and to derive new predictions. Notably, our results indicate how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we developed a test to decide whether the decoding weights of individual neurons are optimal for the task, even without knowing the underlying correlations. We confirmed the practicality of our approach using simulated data from a realistic population model. Thus, our findings provide a theoretical foundation for a growing body of experimental results on choice probabilities and correlations.
