The Neuroscience Lab
summary
A working lab pipeline ties stimulus delivery, hardware control, tracking, analysis and plotting into one Python stack: 360 Hertz virtual reality for rats; Arduino control via pyserial for odors and rewards; OpenCV webcam tracking that replaces a €2000 box; a 3D scanner made by pairing projector and tracker; numpy, pandas, scipy.signal, scikit-learn, neo and others for processing and stats. More than 20+ Python packages appear in the workflow and the virtual environments and analyses demonstrably produce usable results.
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chapters (37)
- 0:00Neuroscience Python talk intro
- 1:02Neuroscience research overview
- 2:31Python in neuroscience experiments
- 3:36Experiment design and sensory input
- 4:19Visual stimuli with PsychoPy
- 5:00Custom graphics and VR
- 6:25Olfactory stimuli and Arduino
- 7:54Network and hardware integration
- 8:57Commercial devices and Python libraries
- 9:59Building 3D scanner from devices
- 10:21Animal tracking with OpenCV
- 11:30Live animal tracking demo
- 12:32PyQt user interfaces
- 14:25Neuroscience data analysis workflow
- 14:47Reading data into Python
- 15:29Interactive Jupyter analysis
- 15:50Neuroscience data formats
- 16:55NumPy for high-dimensional data
- 17:38Data reshaping and linear algebra
- 18:42Pandas for labeled data
- 19:04Signal processing with SciPy
- 20:07Machine learning with scikit-learn
- 20:49Stats with statsmodels
- 21:31Statistical testing with scipy.stats
- 22:37Matplotlib and Seaborn visualization
- 24:02Publishing research with Python
- 24:45Why Python excels in science
- 25:49Python community importance
- 26:35Closing remarks
- 27:17Q&A
- 28:03fMRI data analysis in Python
- 28:47Balancing research and Python dev
- 30:56Learning Python for neuroscience
- 32:00Career in neuroscience
- 32:43VR development challenges
- 33:47Hardware timing precision
- 34:08Python’s research speed impact
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