Next-generation neural recording platform for the Canadian Centre for Behavioural Neuroscience

莱斯布里奇大学加拿大行为神经科学中心（CCBN）的北极星脑动力学小组的五个pi将开发下一代高通量神经记录和行为分析平台，以支持现在和未来的实验。这些研究人员站在解码大脑中记录的神经元集合信息的最前沿，他们使用的是包含48-68根电线的手工设备。然而，神经元的产量往往不足以使用现代数据分析技术进行准确解码。最近的制造技术进步使得探针可以在硅柄上拥有数千个记录位点，这大大提高了神经元的产量，使数据科学方法成为可能。该项目的第一部分将使我们能够现代化我们的基础设施，以便使用这些探测器。为了继续在理解大脑如何形成和使用神经表征来处理信息方面处于领先地位，我们有必要采用机器学习和其他现代统计技术。解码来自神经系统的信息需要我们对感官输入有很好的控制。我们在使用虚拟现实（VR）方面取得了重要进展，可以(1)模拟现实生活条件，(2)引入现实生活中不可能实现的操作，(3)对多模态感官输入进行控制。这个项目的第二部分将允许我们扩展我们的虚拟现实设备的能力，以呈现视觉，听觉，触觉和嗅觉线索，以及食物奖励和喘息惩罚，同时记录神经活动。这项新技术将使我们能够记录更多的神经元，更好地跟踪刺激和输出，这对于深入了解大脑如何处理信息以形成记忆和做出决定是必要的。此外，由探测器实现的自动化数据预处理和分析的进步将使实验更快地完成。为了实现这一目标，北极星脑动力学小组将利用三个重要的能力：1)我们在高密度电生理学方面的经验；2)我们在计算神经科学和数据分析方面的强大背景；3)我们熟练的培训生团队。拟议的RTI拨款将使研究能够发现学习和记忆、决策、运动、癫痫、吸毒成瘾、抑郁和阿尔茨海默病的机制。这也将为学员提供他们在计算神经科学、机器学习和数据科学领域发展成功所需的工具。

Five PIs of the Polaris Brain Dynamics Group at the Canadian Centre for Behavioural Neuroscience (CCBN) at the University of Lethbridge will develop a next-generation, high-throughput, neural recording and behavioral analysis platform that will support experiments now and into the future. These investigators are at the forefront of decoding information from ensembles of neurons recorded in the brain using hand-made devices containing 48-68 wires. However, the yield of neurons is often insufficient for accurate decoding using modern data analytic techniques. Recent manufacturing advances now allow probes to have thousands of recording sites on a silicon shank, which significantly increases neuron yield to enable data-science approaches. The first part of this project will allow us to modernize our infrastructure in order to use these probes. This is necessary for us to adopt machine learning and other modern statistical techniques in order to continue to be leaders in understanding how the brain forms and uses neural representations to process information. Decoding information from neural ensembles requires that we have good control over sensory inputs. We have made important advances in the use of virtual reality (VR) to (1) mimic real-life conditions, (2) introduce manipulations not possible in real-life and (3) exert control over multimodal sensory inputs. This second part of this project will allow us to expand the capacity of our VR apparatus to present visual, auditory, tactile, and olfactory cues, as well as food rewards and air puff punishments, while recording neural activity. This new technology will allow us to record more neurons and better track stimuli and outputs, which are necessary to extract deeper knowledge about how the brain processes information to form memories and make decisions. Furthermore, advances in automated data preprocessing and analysis enabled by the probes will allow experiments to be completed much faster. To accomplish this, the Polaris Brain Dynamics Group will leverage three important competencies: 1) our experience in high-density electrophysiology; 2) our strong background in computational neuroscience and data analysis; and 3) our team of skilled trainees. The proposed RTI grant will enable studies to make discoveries about the mechanisms of learning and memory, decision making, movements, epilepsy, drug addiction, depression, and Alzheimer's disease. This will also give trainees the tools they need to develop successful careers in computational neuroscience, machine learning, and data science.