生物谷報(bào)道：5月8日《神經(jīng)元》（Neuron）雜志上美加科學(xué)家聯(lián)合發(fā)表了一項(xiàng)最新研究成果,，他們繞過(guò)難題，建立了一個(gè)肢體對(duì)于神經(jīng)反應(yīng)的模型,。
是不是在初級(jí)運(yùn)動(dòng)皮層的神經(jīng)元對(duì)精細(xì)的運(yùn)動(dòng)行為（如某個(gè)肌肉的活動(dòng)）進(jìn)行編碼,？抑或是這些神經(jīng)元在對(duì)高層次的運(yùn)動(dòng)行為進(jìn)行編碼？已有的經(jīng)驗(yàn)表明,，要解決這個(gè)問(wèn)題是相當(dāng)困難的,，因?yàn)樵诤艽蟪潭壬希烙?jì)和測(cè)量肢體運(yùn)動(dòng)產(chǎn)生的關(guān)節(jié)力矩和肌肉的力量有很多固有的不確定性,。此次研究人員通過(guò)研究等量肌肉任務(wù)的神經(jīng)元反應(yīng)（比如,，關(guān)節(jié)力矩和肌肉力量可以直接用肢體幾何學(xué)計(jì)算得到）來(lái)繞過(guò)這個(gè)難題。每個(gè)神經(jīng)元的響應(yīng)可以作為一個(gè)“首選”的關(guān)節(jié)力矩向量的線(xiàn)性函數(shù)來(lái)模型化,，這個(gè)模型適合于肢體姿勢(shì)變化時(shí)的神經(jīng)反應(yīng),。
該研究結(jié)果的擬合效果表明，在運(yùn)動(dòng)皮層的神經(jīng)元確實(shí)編碼了運(yùn)動(dòng)行為的動(dòng)力學(xué)特點(diǎn),，并表明神經(jīng)反應(yīng)性能的“優(yōu)先方向”和“增益”是一個(gè)單一回應(yīng)向量的兩個(gè)組成部分,。（生物谷www.bioon.com）
生物谷推薦原始出處：
Neuron，Vol 58, 414-428, 08 May 2008,，Robert Ajemian, Stephen Grossberg
Assessing the Function of Motor Cortex: Single-Neuron Models of How Neural Response Is Modulated by Limb Biomechanics
Robert Ajemian,1, Andrea Green,2 Daniel Bullock,3,4 Lauren Sergio,5 John Kalaska,2, and Stephen Grossberg3,4
1 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 Groupe de recherche sur le système nerveux central (FRSQ), Département de physiologie, Université de Montréal, QC H3C 3J7, Canada
3 Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215, USA
4 Center of Excellence for Learning in Education, Science, and Technology, Boston, MA 02215, USA
5 School of Kinesiology and Health Science, York University, Toronto, ON ON M3J 1P3, Canada
Summary
Do neurons in primary motor cortex encode the generative details of motor behavior, such as individual muscle activities, or do they encode high-level movement attributes? Resolving this question has proven difficult, in large part because of the sizeable uncertainty inherent in estimating or measuring the joint torques and muscle forces that underlie movements made by biological limbs. We circumvented this difficulty by considering single-neuron responses in an isometric task, where joint torques and muscle forces can be straightforwardly computed from limb geometry. The response for each neuron was modeled as a linear function of a “preferred” joint torque vector, and this model was fit to individual neural responses across variations in limb posture. The resulting goodness of fit suggests that neurons in motor cortex do encode the kinetics of motor behavior and that the neural response properties of “preferred direction” and “gain” are dual components of a unitary response vector.
