A neural network that finds a naturalistic solution for the production of muscle activity.
A neural network that finds a naturalistic solution for the production of muscle activity.
复制标题
DOI：
10.1038/nn.4042
复制
发表时间：
2015-07
影响因子：
25
通讯作者：
Shenoy, Krishna V.
中科院分区：
文献类型：
--
作者：
Sussillo, David;Churchland, Mark M.;Kaufman, Matthew T.;Shenoy, Krishna V.
运动皮质中的神经反应如何与运动有关仍是一个悬而未决的问题。我们探索了一种假设，即运动皮质反映的动力学适合于产生时间模式的传出命令。为了使这一假设正式化，我们训练了递归神经网络来复制到达猴子的肌肉活动。模型必须推断可以将简单的输入转化为肌肉活动的时间和空间复杂模式的动力学。对训练模型的分析表明，自然动力学解是一个低维振荡器，它产生必要的多相命令。这种解决方案在单个神经元和种群水平上都与从相同猴子的神经记录中观察到的非常相似。值得注意的是，只有当模型经过优化以找到简单的解决方案时，数据和模拟才会一致。一个吸引人的解释是，经验上观察到的运动皮质动力学可能反映了产生时间模式下降命令问题的简单解决方案。
It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.
登录
查看更多内容
影响因子：
2.5
作者：
Kaufman MT;Churchland MM;Shenoy KV
通讯作者：
Shenoy KV
影响因子：
25
作者：
Laje, Rodrigo;Buonomano, Dean V.
通讯作者：
Buonomano, Dean V.
影响因子：
5.5
作者：
BUYS, EJ;LEMON, RN;MUIR, RB
通讯作者：
MUIR, RB
影响因子：
2.5
作者：
FETZ, EE;CHENEY, PD
通讯作者：
CHENEY, PD
影响因子：
5.7
作者：
Fetz, E E
通讯作者：
Fetz, E E