RFEst v2 is a Python3 toolbox for neural receptive field estimation, featuring methods such as spline-based GLMs, Empirical Bayes with various Gaussian priors, and a few matrix factorization methods.

Spline-based GLMs [1]

The new GLM module unified both vanilla and spline GLMs.

from rfest import GLM

lnp = GLM(distr='poisson', output_nonlinearity='softplus')

# add training data
lnp.add_design_matrix(X_train, dims=[25, ], df=[8, ], smooth='cr', name='stimulus')  # use spline for stimulus filter
lnp.add_design_matrix(y_train, dims=[20, ], df=[8, ], smooth='cr', shift=1,
                      name='history')  # use spline for history filter

# add validation data
lnp.add_design_matrix(X_dev, name='stimulus')  # basis will automatically apply to dev set
lnp.add_design_matrix(y_dev, name='history')

# intialize model parameters
lnp.initialize(num_subunits=1, dt=dt, method='random', random_seed=2046)

# fit model
lnp.fit(y={'train': y_train, 'dev': y_dev},
        num_iters=1000, verbose=100, step_size=0.1, beta=0.01)

Evidence Optimization

• Ridge Regression
• Automatic Relevance Determination (ARD) [2]
• Automatic Smoothness Determination (ASD) [3]
• Automatic Locality Determination (ALD) [4]

from rfest import ASD

asd = ASD(X, y, dims=[5, 20, 15])  # nT, nX, nY
p0 = [1., 1., 2., 2., 2.]  # sig, rho, 𝛿t, 𝛿y, 𝛿x
asd.fit(p0=p0, num_iters=300)

Matrix Factorization

A few matrix factorization methods have been implemented as a submodule (MF).

from rfest.MF import KMeans, semiNMF

For more information, see here.

rfest is available on pypi:

pip install rfest

This will install rfest with CPU support.

Alternative, you can clone this repo into a local directory and install via pip editable mode:

git clone https://github.com/berenslab/RFEst
pip install -e RFEst

If you want GPU support, follow the instructions on the JAX github repository to install JAX with GPU support (before installing rfest). For example, for NVIDIA GPUs, run

pip install -U "jax[cuda12]"

numpy
scipy
sklearn
matplotlib
jax
jaxlib

Tutorial notebooks can be found here: https://github.com/huangziwei/notebooks_RFEst

[1] Huang, Z., Ran, Y., Oesterle, J., Euler, T., & Berens, P. (2021). Estimating smooth and sparse neural receptive fields with a flexible spline basis. Neurons, Behavior, Data Analysis, and Theory, 5(3), 1–30. https://doi.org/10.51628/001c.27578

[2] MacKay, D. J. (1994). Bayesian nonlinear modeling for the prediction competition. ASHRAE transactions, 100(2), 1053-1062.

[3] Sahani, M., & Linden, J. F. (2003). Evidence optimization techniques for estimating stimulus-response functions. In Advances in neural information processing systems (pp. 317-324).

[4] Park, M., & Pillow, J. W. (2011). Receptive field inference with localized priors. PLoS computational biology, 7(10) , e1002219.