[x] Describe your goal, and if possible provide a code snippet with a motivating example.
BrainPy is a useful Python library for analyzing and modeling neuroscience data. However, it currently lacks functionality for fitting models to data. I propose adding a model fitting module to BrainPy that would allow users to easily fit predefined models to their data.
Proposed Implementation:
Create a new module in BrainPy called brainpy.fitting
Support general classes for common neuron and population models used in neuroscience, such as:
Leaky integrate-and-fire models
Hodgkin-Huxley models
Wilson-Cowan models
Neural mass models
Support common fitting algorithms like gradient descent, Nelder-Mead, etc.
The module could also include convenience functions for things like:
Automatically estimating good initial parameters
Performing cross-validation
Statistical model comparison
Model classes should track goodness of fit metrics like R^2, MSE, etc.
Plotting methods to visualize fits against data
Support fitting models to diverse data types: spike trains, LFP, EEG, fMRI, etc.
Include options to parallelize fitting across multiple CPUs or GPUs.
Please:
BrainPy is a useful Python library for analyzing and modeling neuroscience data. However, it currently lacks functionality for fitting models to data. I propose adding a model fitting module to BrainPy that would allow users to easily fit predefined models to their data.
Proposed Implementation:
Create a new module in BrainPy called
brainpy.fittingSupport general classes for common neuron and population models used in neuroscience, such as:
Support common fitting algorithms like gradient descent, Nelder-Mead, etc.
The module could also include convenience functions for things like:
Model classes should track goodness of fit metrics like R^2, MSE, etc.
Plotting methods to visualize fits against data
Support fitting models to diverse data types: spike trains, LFP, EEG, fMRI, etc.
Include options to parallelize fitting across multiple CPUs or GPUs.