bioflax

Content:

• bioflax
  • Introduction
  • Requirements & Installation
  • Data Download
  • Repository structure
  • Use layers
  • Run experiments
  • Usefull References

Introduction

Bioflax provides an unofficial JAX implementation of biologically plausible deep learning algorithms. In particular, Feedback Alignment, Kolen-Pollack, and Direct Feedback alignment are implemented and a framework for running experiments with them is given. The code implements custom Flax modules, which flawlessly integrate with the Flax framework.

The respective algorithms' network structures are depicted in the following scheme. For a more detailed overview please refer to the docs.

| Backpropagation | Feedback Alignment &
Kolen-Pollack | Direct Feedback
Alignment | | --------------------------------------------------------------------- | -------------------------------------------------------------------- | ----------------------------------------------------------------------- | | | | |

Network architectures for different algorithms. Taken from [5].

Requirements & Installation

To run the code on your own machine, run pip install -r requirements.txt.

For the GPU installation of JAX, which is a little more involved please refer to the JAX installation instructions.

For dataloading PyTorch is needed. It has to be installed separately and more importantly, the CPU version must be installed because of inference issues with JAX. Please refer to PyTorch installation instructions.

Data Download

In this first release, the code has built-in support to run experiments on the MNIST dataset, a teacher-student dataset, and a sin-regression dataset. None of these require explicit data download. MNIST will be downloaded automatically on execution and the teacher-student dataset as well as the sin-regression dataset are created on the fly.

Repository structure

Directories and files that ship with the GitHub repo

``` .github/workflows/ runtests.yml Workflow to run unit tests that ensure the functionality of the layer implementations. bioflax/ dataloading.py Dataloading functions. metriccomputation.py Functions for metric computation model.py Layer implementations for FA, KP, and DFA. testlayers.py Unit tests for layer implementations. train.py Training loop code. trainhelpers.py Functions for optimization, training, and evaluation steps. requirements.txt Requirements for running the code. run_train.py Training loop entry point.

```

Directories that may be created on-the-fly:

data/MNIST/raw Raw MNIST data as downloaded. wandb/ Local WandB log file

Use layers

Separately from the rest of the code, the Flax custom modules - the biological layer implementations respectively - can be used to define custom modules (Dense networks) that run with the respective deep learning algorithm. For example, a two-layer Dense network with sigmoid activation in the hidden layer that perfectly integrates with the Flax framework can be created for each of the algorithms as follows:

```python import jax import flax.linen as nn from model import ( RandomDenseLinearFA, RandomDenseLinearKP, RandomDenseLinearDFAOutput, RandomDenseLinearDFAHidden )

class NetworkFA(nn.Module): @nn.compact def call(self, x): x = RandomDenseLinearFA(15)(x) x = nn.sigmoid(x) x = RandomDenseLinearFA(10)(x) return x

class NetworkKP(nn.Module): @nn.compact def call(self, x): x = RandomDenseLinearKP(15)(x) x = nn.sigmoid(x) x = RandomDenseLinearKP(10)(x) return x

Note the differences for DFA. In particular, activations must be handed to the hidden layers and mustn't be on the

computational path elsewhere. Secondly, the hidden layers need the final output dimension as an additional input

class NetworkDFA(nn.Module): @nn.compact def call(self, x): x = RandomDenseLinearDFAHidden(15, 10, nn.sigmoid)(x) x = RandomDenseLinearDFAOutput(10)(x) return x

```

If you need help on how to use modules for actual learning in Flax please refer to the Flax doxumentation.

Run experiments

To run an experiment execute

python run_train.py

which will result in a run with the default configuration. For information about the arguments and their default settings execute one of the following commands

python run_train.py --help python run_train.py --h

Useful References

[1] David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 323(6088), 1986.

[2] Stephen Grossberg. Competitive learning: From interactive activation to adaptive resonance. Cognitive science, 11(1):23–63, 1987.

[3] Francis Crick. The recent excitement about neural networks. Nature, 337:129–132, 1989.

[4] Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, and Colin J. Akerman. Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications, 7(1), 2016.

[5] Arild Nøkland. Direct feedback alignment provides learning in deep neural networks. In Advances in Neural Information Processing Systems, 2016.

[6] J.F. Kolen and J.B. Pollack. Backpropagation without weight transport. In Proceedings of 1994 IEEE International Conference on Neural Networks, 1994.