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.

Supported Methods

Spline-based GLMs [1]

The new GLM module unified both vanilla and spline GLMs.

```python from rfest import GLM

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

add training data

lnp.adddesignmatrix(Xtrain, dims=[25, ], df=[8, ], smooth='cr', name='stimulus') # use spline for stimulus filter lnp.adddesignmatrix(ytrain, dims=[20, ], df=[8, ], smooth='cr', shift=1, name='history') # use spline for history filter

add validation data

lnp.adddesignmatrix(Xdev, name='stimulus') # basis will automatically apply to dev set lnp.adddesignmatrix(ydev, name='history')

intialize model parameters

lnp.initialize(numsubunits=1, dt=dt, method='random', randomseed=2046)

fit model

lnp.fit(y={'train': ytrain, 'dev': ydev}, numiters=1000, verbose=100, stepsize=0.1, beta=0.01) ```

Evidence Optimization

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

```python 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).

python from rfest.MF import KMeans, semiNMF

For more information, see here.

Installation

rfest is available on pypi:

sh 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:

sh 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

sh pip install -U "jax[cuda12]"

Dependencies

numpy
scipy
sklearn
matplotlib
jax
jaxlib

Tutorial

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

Reference

[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.