Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.
Eur Biophys J. 2022 Sep;51(6):503-514. doi: 10.1007/s00249-022-01613-0. Epub 2022 Aug 5.
Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.
培养的神经元网络（CNNs）是研究神经元在受控神经元群体网络中如何进行表示和适应的强大工具。为了确保 CNN 与人工环境的交互，应该提供可靠的开环和闭环操作。在本研究中，我们使用数字微镜设备将光遗传学刺激与微电极阵列（MEA）记录集成在一起，并开发了一种具有 64 个通道接口的改进研究工具，用于神经元网络控制和数据采集。我们确定了我们的配置的理想刺激参数，包括光强度、频率和占空比。这导致了强大且可重复的神经元响应。我们还在新平台中演示了涉及多个双向通道的开环和闭环配置。与以前将光遗传学刺激和 MEA 记录结合使用的方法不同，我们没有使用二进制网格模式，而是为每个电极分配了一个可调节大小的非二进制光学光斑。这种方法允许同时使用多个输入输出通道，并便于调整刺激参数。因此，我们在 64 个通道接口方面取得了进展，每个通道都可以在两个方向上同时进行单独控制，而不会产生任何干扰或中断。所提出的设置满足神经元可塑性、网络编码和表示、放电率和同步的闭环控制研究的要求。开发网络活动闭环控制技术和自适应刺激策略的研究人员将从这个新设置中受益匪浅。
