Neural Data Science A Primer with MATLAB® and Python™
Auteurs : Nylen Erik Lee, Wallisch Pascal
A Primer with MATLAB® and Python? present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience.
This book addresses the snake in the room by providing a beginner?s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.
Part I: Foundations 1. Philosophy 2. From 0 to 0.01
Part II: Neural Data Analysis 3. Wrangling Spike Trains 4. Correlating Spike Trains 5. Analog Signals 6. Biophysical Modeling
Part III: Going Beyond the Data 7. Regression 8. Dimensionality Reduction 9. Classification and Clustering 10. Web Scraping
Pascal Wallisch serves as a professor in the Department of Psychology at New York University where he currently teaches statistics, programming and the use of mathematical tools in neuroscience and psychology. He received his PhD in Psychology from the University of Chicago and worked as a postdoctoral fellow at the Center for Neural Science at New York University. He has a long-term commitment and is dedicated to educational excellence, which was recognized by the “Wayne C. Booth Graduate Student Prize for Excellence in teaching at the University of Chicago and the “Golden Dozen Award at New York University. He co-founded and co-organizes the “Neural Data Science summer course at Cold Spring Harbor Laboratory and co-authored “Matlab for Neuroscientists.
- Includes discussions of both MATLAB and Python in parallel
- Introduces the canonical data analysis cascade, standardizing the data analysis flow
- Presents tactics that strategically, tactically, and algorithmically help improve the organization of code
Date de parution : 05-2017
Ouvrage de 368 p.
19x23.3 cm
Thèmes de Neural Data Science :
Mots-clés :
API; Aliasing; Analog signals; Beta weights; Beyond; Biophysics; Bootstrap; Butterworth; Canonical data analysis cascade; Cell arrays; Chebyshev; Coding; Computational neuroscience; Confidence interval; Convolution; Current; Curve fitting; Data; Data analysis; Data browsing; Data frame; Data munging; Data science; Dimensionality reduction; Dress code; Evoked potentials; Filtering; Find; For loops; Fourier transform; Frequency space; Function; Hanning; Heat map; If statements; Information; K-means; Lasso; Latency to first spike; Levels of coding; Linear regression; Local field potential (LFP); Logistic regression; MATLAB; Matrix; Membrane; Meshgrid; Metadata; Modeling; Multivariate data; Neural data science; Neurons; Neuroscience; Nyquist; Object-oriented programming; Objects; Overfitting; PSTH; Pandas; Plato's cave; Power spectrum; Prediction; Principal component analysis (PCA); Psychopathy; PubMed; Python; R squared; RMSE; Random forest; Random numbers; Raster plot; Regression; Representation; Resampling; Ridge regression; Sample mean; Sampling; Sata; Scikit-learn; Spike; Spike count correlation; Spike train; String parsing; Supervised learning; Support vector machines (SVM); Table; Try/catch; Try/except; Unsupervised learning; Variable; Vector; Web scraping; XML