Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.
Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.
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DOI：
10.3389/fnins.2016.00028
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发表时间：
2016
影响因子：
4.3
通讯作者：
Lytton WW
中科院分区：
文献类型：
--
作者：
Dura-Bernal S;Li K;Neymotin SA;Francis JT;Principe JC;Lytton WW
神经刺激可以用作通过调节神经活动来引起自然感觉或行为的工具。这可以潜在地用于减轻脑损伤或神经障碍的损害。然而，为了获得最佳的刺激序列，有必要开发神经控制方法，例如通过构建目标系统的逆模型。对于真实的大脑来说，这可能是非常具有挑战性的，并且通常是不可行的，因为它需要反复刺激神经系统以获得足够的探测数据，并且依赖于毫无根据的平稳性假设。相比之下，详细的大脑模拟可能会提供一个替代测试平台，用于了解正在进行的神经活动和外部刺激之间的相互作用。与真实的大脑不同，人工系统可以被广泛而精确地探测，并且可以随时获得详细的输出信息。在这里，我们采用了一个尖峰网络模型的感觉运动皮层训练，以驱动一个现实的虚拟肌肉骨骼手臂，以达到目标。然后，通过使神经元沉默或移除突触连接来扰乱网络，以模拟损伤。所有损伤导致在到达任务期间的显著行为障碍。然后用一组单细胞和多细胞刺激系统地探测剩余的细胞，并将结果用于建立神经系统的逆模型。使用核自适应滤波方法构建逆模型，并用于预测恢复损伤前神经活动所需的神经刺激模式。将衍生的神经刺激应用于受损网络改善了到达行为表现。本工作提出了一种新的神经控制方法，并为利用仿生脑模型开发和评估神经控制器以恢复受损脑区的功能和相应的运动行为提供了理论基础。
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.