Preprint available at https://www.biorxiv.org/content/10.1101/516484v1

Journal paper available at https://www.cell.com/cell/fulltext/S0092-8674(19)30391-5

Technical paper describing extensive (in silico) tests at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007973

Update (19/4/3): Added rudimentary support for PyTorch. See below for details.

Update (19/5/10): PyTorch can now be used instead of caffe.

Update (merged 20/6/22): Restructured repository; added generators, optimizers, tests, and simulation data

XDream (Extending DeepDream with real-time evolution for activity maximization in real neurons) is a package for visualizing the preferred input of units in non-differentiable visual systems—such as the visual cortex—through activation maximization.

Well-known network visualization techniques such as DeepDream depend on the objective function being differentiable down to the input pixels. In cases such as the visual cortext in the brain, these gradients are unavailable.

However, visualization without gradients can be framed as a black-box optimization problem. Such a problem can be approached by an iterative process where

1. Images are shown;
2. Each image receives a scalar "score";
3. Scores are used to optimize the images.

Two optimizers are implemented here: a simple genetic algorithm and a gradient estimation algorithm based on finite differences.

A further component—generative neural networks—is introduced to make the search space diverse yet still tractable. Instead of optimizing images, the optimization algorithm manipulates "codes" used to generate images. Since codes are the learned input to a generative network, searching in code space avoids searching uninteresting noise images, for example salt-and-pepper noise.

As an example, we use the DeepSiM generators from Dosovitskiy & Brox, 2016. The interface to DeepSiM generators depends on caffe, but is modular and can be replaced by other generative neural networks and indeed any other image parameterization (e.g., Yamane et al., 2008, Mordvintsev et al., 2018 ).

• The PyTorch library or the caffe library. Main functionalities are available with both, but some legacy functionalities have not been ported to pytorch.

  To install PyTorch, please visit the official website for instructions.

  To install caffe, on ubuntu > 17.04 you can use

  sudo apt install caffe-cpu

  for CPU-only version, or

  sudo apt install caffe-cuda

  for CUDA version.

  Caffe can also be installed on Windows using Anaconda/Minoconda, with

  conda install caffe -c willyd

  This will install a pre-compiled version of caffe. However, this package seems to depend on some Visual Studio 2015 components. They can be installed with the component "Programming Languages/Visual C++/Common Tools for Visual C++ 2015" in Visual Studion Community 2015.

  Alternatively, caffe can be built from source.

• local_settings.py. Copy it from local_settings.example.py and modify the contents to match your system.

• Pretrained generative networks. The DeepSiM generators have been converted into pytorch from caffe. They are defined in torch_nets/deepsim.py, and the weights are available here.

  The original caffe models can be downloaded from here. The prototxt files (slightly modified from original) are included in the prototxt folder.

  Please make sure the paths defined in net_catalogue.py match the downloaded .caffemodel, .prototxt, and/or .pt files.

• For the demo, the pytorch pretrained alexnet model. It is available here. Please save the model files to the paths defined in net_catalogue.py. Other vision models, such as CaffeNet for caffe, can also be used.

Run

python demo.py

The demo uses pytorch by default.

Experiment modules (EphysExperiment & CNNExperiment in Experiments.py) and scripts (experiment.py & experiment_CNN.py) are included to exemplify how to define/control an experiment using this package.

To extend this package for use with your experimental system, at the least you may need to extend the _get_scores() method of WithIOScorer. For example, in EPhysScorer, we write online recording data in a .mat file and the _get_scores() method reads it from disk.

Some additional tools are included for creating the initial generation of codes (for genetic algorithm) and empirically optimizing hyperparameters.

• Restructure repository
• Add data and plotting code (characterizing performance in simulated experiments with CNNs)
• Clean up code