Neural Encoding of Reaches in a Linear Cortical Model.
Neural Encoding of Reaches in a Linear Cortical Model.
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线性皮质模型中触及范围的神经编码。
DOI：
10.1109/embc46164.2021.9630295
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sarma,SrideviV
中科院分区：
文献类型：
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作者：
Greene,Patrick;Schieber,MarcH;Sarma,SrideviV
为了有效地控制手臂，运动皮层神经元必须产生复杂的激活模式，这些激活模式随着手臂的位置和方向以及到达方向而变化。为了更好地理解这样一个微调的动力系统是如何产生的，以及它的基本组织原理是什么，我们开发了一个运动皮层模型，作为一个线性动力系统，反馈耦合到猕猴手臂的两关节模型。通过根据惩罚手和目标之间的误差的目标函数以及神经和肌肉能量的使用来优化神经群体之间的连接，我们表明可以自然地获得运动皮层的某些特性，例如肌肉协同作用。我们还证明，优化过程产生了一个稳定的神经系统，其中物理空间中的目标被映射到神经状态空间中吸引固定点。最后，我们表明这个优化过程产生具有复杂空间和时间激活模式的神经单元。
To effectively control the arm, motor cortical neurons must produce complex patterns of activation that vary with the position and orientation of the arm and reach direction. In order to better understand how such a finely tuned dynamical system could arise and what its basic organizing principles are, we develop a model of the motor cortex as a linear dynamical system with feedback coupled to a two-joint model of the macaque arm. By optimizing the connections between neural populations with respect to an objective function that penalizes error between hand and target, as well as neural and muscular energy use, we show that certain properties of the motor cortex, such as muscle synergies, can naturally be obtained. We also demonstrate that the optimization process produces a stable neural system in which targets in the physical space are mapped to attracting fixed points in the neural state space. Finally, we show that this optimization process produces neural units with complex spatial and temporal activation patterns.