Change log

All notable changes between Rockpool releases will be documented in this file.

[v2.4] – 2022-08

Major changes

  • Linear... modules now do not have a bias parameter, by default.

Added

  • Support for Xylo SNN core v2, via XyloSim. Including biases and quantisation support; mapping and deployment for Xylo SNN core v2 (SYNS61201)

  • Added support for Xylo-A2 test board, with audio recording support from Xylo AFE (AFESamna and XyloSamna)

  • Support for an LIF neuron including a trainable adaptive threshold (aLIFTorch). Deployable to Xylo

  • New module BooleanState, which maintains a boolean state

  • Support for membrane potential training using LIFExodus

Changed

  • Xylo package support for HW versioning (SYNS61300; SYNS61201)

  • Ability to return events, membrane potentials or synaptic currents as output from XyloSim and XyloSamna

  • Enhanced Xylo mapper to be more lenient about weight matrix size β€” now assumes missing weights are zero

  • Xylo mapper is now more lenient about HW constraints, permitting larger numbers of input and output channels than supported by existing HDKs

  • Xylo mapper supports a configurable number of maxmimum hidden and output neurons

  • Running black is enforced by the CI pipeline

  • Linear... modules now export bias parameters, if they are present

  • Linear... modules now do not include bias parameters by default

  • Xylo mapper now raises a warning if any linear weights have biases

  • LIFSlayer renamed to LIFExodus, corresponding to sinabs.exodus library name change

  • Periodic exponetial surrogate function now supports training thresholds

Fixed

  • Fixes related to torch modules moved to simulation devices

  • Fixed issue in dropout.py, where if jax was missing an ImportError was raised

  • Fixed an issue with Constant torch parameters, where deepcopy would raise an error

  • Fixed issue with newer versions of torch; torch v1.12 is now supported

  • Updated to support changes in latest jax api

  • Fixed bug in WavesenseNet, where neuron class would not be checked properly

  • Fixed bug in channel_quantize, where unquantized weights were returned instead of quantized weights

Deprecated

  • LIFSlayer is now deprecated

[v2.3.1] – 2022-03-24

Hotfix

  • Improved CI pipeline such that pipline is not blocked with sinabs.exodus cannot be installed

  • Fixed UserWarning raised by some torch-backed modules

  • Improved some unit tests

[v2.3] – 2022-03-16

Added

  • Standard dynamics introduced for LIF, Rate, Linear, Instant, ExpSyn. These are standardised across Jax, Torch and Numpy backends. We make efforts to guarantee identical dynamics for the standard modules across these backends, down to numerical precision

  • LIF modules can now train threhsolds and biases as well as time constants

  • New JaxODELIF module, which implements a trainable LIF neuron following common dynamical equations for LIF neurons

  • New addition of the WaveSense network architecture, for temporal signal processing with SNNs. This is available in rockpool.networks, and is documented with a tutorial

  • A new system for managing computational graphs, and mapping these graphs onto hardware architectures was introduced. These are documented in the Xylo quick-start tutorial, and in more detail in tutorials covering Computational Graphs and Graph Mapping. The mapping system performs design-rule checks for Xylo HDK

  • Included methods for post-traning quantisation for Xylo, in rockpool.transform

  • Added simulation of a divisive normalisation block for Xylo audio applications

  • Added a Residual combinator, for convenient generation of networks with residual blocks

  • Support for sinabs layers and Exodus

  • Module, JaxModule and TorchModule provide facility for auto-batching of input data. Input data shape is (B, T, Nin), or (T, Nin) when only a single batch is provided

  • Expanded documentation on parameters and type-hinting

Changed

  • Python > 3.6 is now required

  • Improved import handling, when various computational back-ends are missing

  • Updated for new versions of samna

  • Renamed Cimulator -> XyloSim

  • Better parameter handling and rockpool/torch parameter registration for Torch modules

  • (Most) modules can accept batched input data

  • Improved / additional documentation for Xylo

Fixed

  • Improved type casting and device handling for Torch modules

  • Fixed bug in Module, where modules() would return a non-ordered dict. This caused issues with JaxModule

Removed

  • Removed several obsolete Layers and Networks from Rockpool v1

[v2.2] – 2021-09-09

Added

  • Added support for the Xylo development kit in .devices.xylo, including several tutorials

  • Added CTC loss implementations in .training.ctc_loss

  • New trainable torch modules: LIFTorch and others in .nn.modules.torch, including an asynchronous delta modulator UpDownTorch

  • Added torch training utilities and loss functions in .training.torch_loss

  • New TorchSequential class to support Sequential combinator for torch modules

  • Added a FFwdStackTorch class to support FFwdStack combinator for torch modules

Changed

  • Existing LIFTorch module renamed to LIFBitshiftTorch; updated module to align better with Rockpool API

  • Improvements to .typehints package

  • TorchModule now raises an error if submodules are not Torchmodules

Fixed

  • Updated LIF torch training tutorial to use new LIFBitshiftTorch module

  • Improved installation instructions for zsh

[v2.1] – 2021-07-20

Added

  • πŸ‘Ή Adversarial training of parameters using the Jax back-end, including a tutorial

  • 🐰 β€œEaster” tutorial demonstrating an SNN trained to generate images

  • πŸ”₯ Torch tutorials for training non-spiking and spiking networks with Torch back-ends

  • Added new method nn.Module.timed(), to automatically convert a module to a TimedModule

  • New LIFTorch module that permits training of neuron and synaptic time constants in addition to other network parameters

  • New ExpSynTorch module: exponential leak synapses with Torch back-end

  • New LinearTorch module: linear model with Torch back-end

  • New LowPass module: exponential smoothing with Torch back-end

  • New ExpSmoothJax module: single time-constant exponential smoothing layer, supporting arbitrary transfer functions on output

  • New softmax and log_softmax losses in jax_loss package

  • New utilities.jax_tree_utils package containing useful parameter tree handling functions

  • New TSContinuous.to_clocked() convenience method, to easily rasterise a continuous time series

  • Alpha: Optional _wrap_recorded_state() method added to nn.Module base class, which supports wrapping recorded state dictionaries as TimeSeries objects, when using the high-level TimeSeries API

  • Support for add_events flag for time-series wrapper class

  • New Parameter dictionary classes to simplify conversion and handling of Torch and Jax module parameters

    • Added astorch() method to parameter dictionaries returned form TorchModule

  • Improved type hinting

Changed

  • Old LIFTorch module renamed to LIFBitshiftTorch

  • Kaiming and Xavier initialisation support for Linear modules

  • Linear modules provide a bias by default

  • Moved filter_bank package from V1 layers into nn.modules

  • Update Jax requirement to > v2.13

Fixed

  • Fixed binder links for tutorial notebooks

  • Fixed bug in Module for multiple inheritance, where the incorrect __repr__() method would be called

  • Fixed TimedModuleWrapper.reset_state() method

  • Fixed axis limit bug in TSEvent.plot() method

  • Removed page width constraint for docs

  • Enable FFExpSyn module by making it independent of old RRTrainedLayer

Deprecated

  • Removed rpyc dependency

Removed

[v2.0] – 2021-03-24

  • New Rockpool API. Breaking change from v1.x

  • Documentation for new API

  • Native support for Jax and Torch backends

  • Many v1 Layers transferred

[v1.1.0.4] – 2020-11-06

  • Hotfix to remove references to ctxctl and aiCTX

  • Hotfix to include NEST documentation in CI-built docs

  • Hotfix to include change log in build docs

[v1.1] – 2020-09-12

Added

  • Considerably expanded support for DenΓ¨ve-Machens spike-timing networks, including training arbitrary dynamical systems in a new RecFSSpikeADS layer. Added tutorials for standard D-M networks for linear dynamical systems, as well as a tutorial for training ADS networks

  • Added a new β€œIntro to SNNs” getting-started guide

  • A new β€œsharp points of Rockpool” tutorial collects the tricks and traps for new users and old

  • A new Network class, JaxStack, supports stacking and end-to-end gradient-based training of all Jax-based layers. A new tutorial has been added for this functionality

  • TimeSeries classes now support best-practices creation from clock or rasterised data. TSContinuous provides a .from_clocked() method, and TSEvent provides a .from_raster() method for this purpose. .from_clocked() a sample-and-hold interpolation, for intuitive generation of time series from periodically-sampled data.

  • TSContinuous now supports a .fill_value property, which permits extrapolation using scipy.interpolate

  • New TSDictOnDisk class for storing TimeSeries objects transparently on disk

  • Allow ignoring data points for specific readout units in ridge regression Fisher relabelling. To be used, for example with all-vs-all classification

  • Added exponential synapse Jax layers

  • Added RecLIFCurrentIn_SO layer

Changed

  • TSEvent time series no longer support creation without explicitly setting t_stop. The previous default of taking the final event time as t_stop was causing too much confusion. For related reasons, TSEvent now forbids events to occur at t_stop

  • TimeSeries classes by default no longer permit sampling outside of the time range they are defined for, raising a ValueError exception if this occurs. This renders safe several traps that new users were falling in to. This behaviour is selectable per time series, and can be transferred to a warning instead of an exception using the beyond_range_exception flag

  • Jax trainable layers now import from a new mixin class JaxTrainer. THe class provides a default loss function, which can be overridden in each sub-class to provide suitable regularisation. The training interface now returns loss value and gradients directly, rather than requiring an extra function call and additional evolution

  • Improved training method for JAX rate layers, to permit parameterisation of loss function and optimiser

  • Improved the ._prepare_input...() methods in the Layer class, such that all Layers that inherit from this superclass are consistent in the number of time steps returned from evolution

  • The Network.load() method is now a class method

  • Test suite now uses multiple cores for faster testing

  • Changed company branding from aiCTX -> SynSense

  • Documentation is now hosted at https://rockpool.ai

Fixed

  • Fixed bugs in precise spike-timing layer RecSpikeBT

  • Fixed behavior of Layer class when passing weights in wrong format

  • Stability improvements in DynapseControl

  • Fix faulty z_score_standardization and Fisher relabelling in RidgeRegrTrainer. Fisher relabelling now has better handling of differently sized batches

  • Fixed bugs in saving and loading several layers

  • More sensible default values for VirtualDynapse baseweights

  • Fix handling of empty channels argument in TSEvent._matching_channels() method

  • Fixed bug in Layer._prepare_input, where it would raise an AssertionError when no input TS was provided

  • Fixed a bug in train_output_target, where the gradient would be incorrectly handled if no batching was performed

  • Fixed to_dict method for FFExpSynJax classes

  • Removed redundant _prepare_input() method from Torch layer

  • Many small documentation improvements


[v1.0.8] – 2020-01-17

Added

  • Introduced new TimeSeries class method concatenate_t(), which permits construction of a new time series by concatenating a set of existing time series, in the time dimension

  • Network class now provides a to_dict() method for export. Network now also can treat sub-Networks as layers.

  • Training methods for spiking LIF Jax-backed layers in rockpool.layers.training. Tutorial demonstrating SGD training of a feed-forward LIF network. Improvements in JAX LIF layers.

  • Added filter_bank layers, providing layer subclasses which act as filter banks with spike-based output

  • Added a filter_width parameter for butterworth filters

  • Added a convenience function start_at_zero() to delay TimeSeries so that it starts at 0

  • Added a change log in CHANGELOG.md

Changed

  • Improved TSEvent.raster() to make it more intuitive. Rasters are now produced in line with time bases that can be created easily with numpy.arange()

  • Updated conda_merge_request.sh to work for conda feedstock

  • TimeSeries.concatenate() renamed to concatenate_t()

  • RecRateEuler warns if tau is too small instead of silently changing dt

Fixed or improved

  • Fixed issue in Layer, where internal property was used when accessing ._dt. This causes issues with layers that have an unusual internal type for ._dt (e.g. if data is stored in a JAX variable on GPU)

  • Reduce memory footprint of .TSContinuous by approximately half

  • Reverted regression in layer class .RecLIFJax_IO, where dt was by default set to 1.0, instead of being determined by tau_...

  • Fixed incorrect use of Optional[] type hints

  • Allow for small numerical differences in comparison between weights in NEST test test_setWeightsRec

  • Improvements in inline documentation

  • Increasing memory efficiency of FFExpSyn._filter_data by reducing kernel size

  • Implemented numerically stable timestep count for TSEvent rasterisation

  • Fixed bugs in RidgeRegrTrainer

  • Fix plotting issue in time series

  • Fix bug of RecRateEuler not handling dt argument in __init__()

  • Fixed scaling between torch and nest weight parameters

  • Move contains() method from TSContinuous to TimeSeries parent class

  • Fix warning in RRTrainedLayer._prepare_training_data() when times of target and input are not aligned

  • Brian layers: Replace np.asscalar with float


[v1.0.7.post1] – 2019-11-28

Added

  • New .Layer superclass .RRTrainedLayer. This superclass implements ridge regression for layers that support ridge regression training

  • .TimeSeries subclasses now add axes labels on plotting

  • New spiking LIF JAX layers, with documentation and tutorials .RecLIFJax, .RecLIFJax_IO, .RecLIFCurrentInJax, .RecLIFCurrentInJAX_IO

  • Added save and load facilities to .Network objects

  • ._matching_channels() now accepts an arbitrary list of event channels, which is used when analysing a periodic time series

Changed

  • Documentation improvements

  • :py:meth:.TSContinuous.plot method now supports stagger and skip arguments

  • .Layer and .Network now deal with a .Layer.size_out attribute. This is used to determine whether two layers are compatible to connect, rather than using .size

  • Extended unit test for periodic event time series to check non-periodic time series as well

Fixed

  • Fixed bug in TSEvent.plot(), where stop times were not correctly handled

  • Fix bug in Layer._prepare_input_events(), where if only a duration was provided, the method would return an input raster with an incorrect number of time steps

  • Fixed bugs in handling of periodic event time series .TSEvent

  • Bug fix: .Layer._prepare_input_events was failing for .Layer s with spiking input

  • TSEvent.__call__() now correctly handles periodic event time series


[v1.0.6] – 2019-11-01

  • CI build and deployment improvements


[v1.0.5] – 2019-10-30

  • CI Build and deployment improvements


[v1.0.4] – 2019-10-28

  • Remove deployment dependency on docs

  • Hotfix: Fix link to Black

  • Add links to gitlab docs


[v1.0.3] – 2019-10-28

  • Hotfix for incorrect license text

  • Updated installation instructions

  • Included some status indicators in readme and docs

  • Improved CI

  • Extra meta-data detail in setup.py

  • Added more detail for contributing

  • Update README.md


[v1.0.2] – 2019-10-25

  • First public release