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From Master of Neuroscience Wiki
- 2017: Change detection: The DivInE-Model
- 2017: Computation Spike by Spike
- 2017: Contour Integration
- 2017: Natural scenes and sparse coding in visual cortex
- 2017: Oscillations and information routing: CTC model
- 2020: Deep Networks and Tensor Flow
- 2020: Divisive Normalization -- a Universal Concept for Adaptive Dynamics and Function of Cortical Circuits
- 2020: Recurrent networks: Temporal dynamics and synchronization
- 2022: Deep Networks and Pytorch
- 2022: Divisive inhibition: a dynamical circuit for change detection
- 2022: Preparation -- Python class with and without classes
- 2022: Synchronization and dynamic oscillations in the visual system
- Advanced Indexing
- Animation and Slider
- Argh: Organize your command line arguments
- Assert
- Austin: Time and memory profiling
- Available dtypes
- Basic Commands and Variables
- Basic Math Operations
- Basic Structure of a Computer
- Basics
- Basics with Python / Matlab
- Beyond normal np.save
- Boolean matricies and logic functions
- Broadcasting: Automatic adaption of dimensions
- Built-in Functions
- Built-in Keywords
- Check if the port for torchrun is open via ncat
- Class
- Collection of distinct hashable objects -- set and frozenset
- Computer admin tutorials
- Concatenate Matrices and arrays
- Config VS Code (Insiders)
- Connor Stevens
- Constants
- Convert other data into numpy arrays e.g. asarray
- Converting the original MNIST files into numpy
- Creating networks
- Creating order via sub-directories: os.makedirs
- Data augmentation
- Dataclass
- Datasets
- Dealing with Matlab files
- Dealing with the main diagonal / triangles of a matrix
- Dict
- Differential Equations
- Dimensions and shape
- DivInE-model for MT neurons
- Dnf and error: rpmdbNextIterator: skipping
- Einstein summation
- Examples
- Exceptions (try / except)
- Expanding Python with C++ modules
- Exponential Integrate-and-Fire
- Extending an existing matrix: tile, repeat, pad
- FFT
- FastICA
- Files
- Finding files in a directory: glob
- Fisher Exact Test: Test if your performance difference is significant
- Flat
- Flip, rot90, and roll a matrix
- Flow Control: for-loop
- Flow Control: if, elif, else
- Flow Control: match case
- Flow Control: while, pass, break, continue
- Flow Control Overview
- Flow chart for baking bread
- Flow chart symbols
- Formatted String Literals
- Functions
- Get CUDA ready!
- Get the API key
- Git jupyterlab
- Hello, Python
- How to read a webcam with CV2
- How to take advantage of a learning rate scheduler for your non-Pytorch project
- How to take advantage of an optimizer for your non-Pytorch project
- Importing Modules
- Input, print, string, int, float
- Instantanious Spectral Coherence
- Integration and Differentiation
- Interfacing Data
- JSON and dict for parameter files
- KMeans
- K Nearest Neighbours (pure numpy)
- Layers
- Leaky Integrate-and-Fire
- Linear algebra
- Linearize the spectral coherence
- List Comprehensions
- Lists
- Logging
- Logi verwenden
- Machine Learning Resources
- Main Page
- Making a matrix from numerical ranges
- Making a new matrix
- Manipulation of integers and their bits
- Math functions
- Matlab Data Analysis
- Matlab Graphics
- Matlab is also just a Python package
- Memory layout of Numpy matrices
- Merging matrices
- Meshgrid
- Ndenumerate
- Ndindex
- Nditer provides many ways to visit all the elements of one or more arrays
- Nested iters
- New matrix
- Notizen zu Cloud-Instanzen und Services
- NumPy for MATLAB users
- Numba: Numpy just in time compiler -- speeding Numpy up
- Numpy and JSON over Pandas
- OpenCV2: Play, write, read a video
- Open Source Tools
- Organizing parameters: dataclasses and dataconf
- Overview
- Overview of the available functions
- PCA
- Pickle: save and load Python objects
- Piecewise
- Preperations
- ProcessPoolExecutor: A fast way to implement multiprocessing
- Programming Recommendations
- Psutil vs os.cpu count: How many "CPUs" do I have?
- PyBind11 Stub-Generation
- PyWavelets: Wavelet Transforms in Python
- Python Scopes and Namespaces
- Python Tutorial
- Python installation
- Quadratic Integrate-and-Fire
- ROC (pure numpy)
- Random numbers the non-legacy way
- Ravel and UnRavel
- References
- Remove a common signal from your data with SVD
- Replace the automatic autograd with your own torch.autograd.Function
- Replacing an inner for loop with apply along axis
- Representation of Numbers in the Computer
- Reshape and flatten
- Resize: Compensation for bad planning? Don't!
- Rfft and spectral power
- Running Python remotely from Matlab
- S1 Advanced programming and data analysis
- Save and load
- Sci-kit Overview
- Scipy.signal: Butterworth low, high and band-pass
- Set printoptions
- Simple plot and imshow examples
- Slices and Views
- Spectral power and averaging over trials
- Stack and Split, Compress
- Statistics
- Style Rulez
- Subplot
- Subplots and gridspec: A more flexible placement
- Support Vector Machine
- Symbolic Computation
- System upgrades with dnf and what to do if it failes
- Systematic Programming
- TQDM: Make your progress visible
- Task 1 --Mandatory Tasks
- Task 1 -- Classycal neurons: Simulation and Mathematical Anaylsis
- Task 2 -- Collective coherent cortices: Data analysis
- Task 2 -- Mandatory Tasks
- Task 2 -- Voluntary Tasks: More Information!
- Task 2 -- Voluntary Tasks: Preprocessing
- Tensorflow / Keras A fast non-introduction
- The N-dimensional array (ndarray)
- The Python Standard Library
- The fast and furious way (CPU and GPU CUDA)
- Thunderbird und der NextCloud Kalender
- Torchrun for multi-node but single GPU -- checking for network problems
- Train the network
- Trim Zeros of a 1d array
- Truth Value Testing
- Tuple
- Type annotations
- Unfold: How to manually calculate the indices for a sliding 2d window
- Uni Datastorage Logi
- Unique
- Using spec kit and copilot
- VS Code Debugging
- VS Code Markdown
- VS Code Microsoft Tutorials
- VS Code Python Interactive window
- VS Code Working remotely via ssh
- VS Code configuration
- VS Code installation
- Vectorization and Vector Calculus in Matlab
- Virtuelle System für Docker vorbereiten
- When a normal dnf update fails
- Where
- Wie benutze ich Vaultwarden
- Write your own layer
- Xpra
- ZeroMQ: Microservices as well as connecting computers via message queue
- Zip
