Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.
Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.
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DOI：
10.1007/s00422-020-00851-9
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发表时间：
2020-12
影响因子：
1.9
通讯作者：
Chiel HJ
中科院分区：
文献类型：
--
作者：
Webster-Wood VA;Gill JP;Thomas PJ;Chiel HJ
动物表现出显著的行为灵活性和多功能控制能力，这对机器人系统来说仍然是一个挑战。动物多功能性的神经和形态学基础可以为机器人控制器提供生物灵感的来源。然而，许多现有的生物神经网络建模方法依赖于计算昂贵的模型，往往只关注神经系统，往往忽略了外围的生物力学。因此，虽然这些模型是神经科学的优秀工具，但它们无法在真实的时间内预测功能行为，而这是机器人控制的关键能力。为了满足实时多功能控制的需要，我们开发了一个混合布尔模型框架，能够以比真实的时间更快的速度对神经爆发活动和简单的生物力学进行建模。使用这种方法，我们提出了一个多功能的模型，定量再现三个关键的喂养行为（咬，吞咽，和拒绝），演示行为切换响应外部感官线索，并结合了已知的神经连接和一个简单的仿生机械模型的喂养装置。我们证明，该模型可用于制定可检验的假设，并讨论这种方法对机器人控制和神经科学的影响。
Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
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影响因子：
16.2
作者：
Bidaye, Salil S.;Laturney, Meghan;Chang, Amy K.;Liu, Yuejiang;Bockemuehl, Till;Bueschges, Ansgar;Scott, Kristin
通讯作者：
Scott, Kristin
影响因子：
1.9
作者：
Bluemel, Marcus;Guschlbauer, Christoph;Bueschges, Ansgar
通讯作者：
Bueschges, Ansgar
影响因子：
1.9
作者：
Bluemel, Marcus;Hooper, Scott L.;Guschlbauer, Christoph;White, William E.;Bueschges, Ansgar
通讯作者：
Bueschges, Ansgar
影响因子：
5.3
作者：
Chiel, Hillel J.;Ting, Lena H.;Hartmann, Mitra J. Z.
通讯作者：
Hartmann, Mitra J. Z.
影响因子：
1.9
作者：
Bazenkov, Nikolay, I;Boldyshev, Boris A.;Kuznetsov, Oleg P.
通讯作者：
Kuznetsov, Oleg P.