A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Related tags

Deep Learning rockpool
Overview

Rockpool

PyPI - Package Conda Documentation Status PyPI - Python Version Black - formatterDOI

Noodle

Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build networks, simulate, train and test them, deploy them either in simulation or on event-driven neuromorphic compute hardware. Rockpool provides layers with a number of simulation backends, including Brian2, NEST, Torch, JAX, Numba and raw numpy. Rockpool is designed to make machine learning based on SNNs easier. It is not designed for detailed simulation of biological networks.

Documentation and getting started

The best place to start with Rockpool is the documentation, which contains several tutorials and getting started guides.

The documentation is hosted online: https://rockpool.ai/

Installation instructions

Use pip to install Rockpool and required dependencies

$ pip install rockpool --user

The --user option installs the package only for the current user.

If you want to install all the extra dependencies required for Brian, PyTorch and Jax layers, use the command

$ pip install rockpool[all] --user

NEST-backed modules

The NEST simulator cannot be installed using pip. Please see the documentation for NEST at [https://nest-simulator.readthedocs.io/en/latest/] for instructions on how to get NEST running on your system.

License

Rockpool is released under a AGPL license. Commercial licenses are available on request.

Contributing

Fork the public repository at https://github.com/SynSense/rockpool, then clone your fork.

$ git clone https://github.com/your-fork-location/rockpool.git rockpool

Install the package in development mode using pip

$ cd rockpool
$ pip install -e . --user

or

$ pip install -e .[all] --user

The main branch is development. You should commit your modifications to a new feature branch.

$ git checkout -b feature/my-feature develop
...
$ git commit -m 'This is a verbose commit message.'

Then push your new branch to your repository

$ git push -u origin feature/my-feature

When you're finished with your modifications, make a merge request on github.com, from your branch in your fork to https://github.com/SynSense/rockpool.

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Comments
  • Training with JAX tutorial won't work because `optimizers` moved to `optax`

    Training with JAX tutorial won't work because `optimizers` moved to `optax`

    Hello,

    JAX has moved the jax.experimental.optimizers to optax (see here)

    And the vanilla installation of JAX won't give access to the experimental module. Therefore, this tutorial breaks.

    Solving it is not as simple as changing this: from jax.experimental.optimizers import adam to this: from optax import adam

    because optax uses different functions to update the parameters. I ll try to solve it with my null understanding of JAX and optax, if I come to a fix I ll post it here

    I am using MacOS Ventura 13.0 Python 3.10.8 JAX Version: 0.3.25 optax Version: 0.1.4 rockpool Version 2.4.2

    opened by aquaresima 2
  • Getting error when trying to start training of LIF Model with TSEvents as input: ValueError: Input has wrong neuron dimension

    Getting error when trying to start training of LIF Model with TSEvents as input: ValueError: Input has wrong neuron dimension

    I am trying to build simple network with two layers of LIF neurons of size 50 and 8 respectively with spike train input:

    # - Define the network size
input_size = 50
hidden_size = 50
output_size = 8

# - Build a sequential stack of modules
sequential_model = Sequential(
    LIF((input_size, hidden_size)),
    LIF((hidden_size, output_size))
)

    then defining TSEvent:

    input_spike_events = TSEvent(
    times=spikes[1],
    channels=spikes[0],
    t_start=0,
    t_stop=1000,
    num_channels=50
)

    where spikes is ndarray of shape 2xN, with N - number of spike, spikes[1] - timepoint of spikes, and spikes[0] - respective spiking channels

    print(input_spike_events)

    produces:

    non-periodic `TSEvent` object `unnamed` from t=0.0 to 1000.0. Channels: 50. 
Events: 244

    But when I call:

    output, new_state, _ = sequential_model(input_spike_events)

    I get an error:

    ValueError: Input has wrong neuron dimension. It is 2, must be 50

    From what i see the issue is in /rockpool/nn/modules/module.py inside the function _auto_batch, line 763, which incorrectly derives n_connections based on data.shape instead of using num_channels parameter of TSEvent object

    rockpool v2.4.2

    opened by fire-papaya 1
Owner
SynSense
SynSense
GitHub
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