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Release History

2.1.0 (unreleased)

API changes

  • Spiking LIF neuron models now accept an additional argument, min_voltage. Voltages are clipped such that they do not drop below this value (previously, this was fixed at 0). (#666)
  • Process objects can now be passed directly as node outputs, making them easier to use. The Process interface is also improved and is currently the same as the Synapse interface. However, further improvements are pending, and the current implementation SHOULD NOT BE RELEASED! (#652)
  • The PES learning rule no longer accepts a connection as an argument. Instead, error information is transmitted by making a connection to the learning rule object (e.g., nengo.Connection(error_ensemble, connection.learning_rule). (#344, #642)
  • The modulatory attribute has been removed from nengo.Connection. This was only used for learning rules to this point, and has been removed in favor of connecting directly to the learning rule. (#642)
  • Connection weights can now be probed with nengo.Probe(conn, 'weights'), and these are always the weights that will change with learning regardless of the type of connection. Previously, either decoders or transform may have changed depending on the type of connection; it is now no longer possible to probe decoders or transform. (#729)
  • A version of the AssociativeMemory SPA module is now available as a stand-alone network in nengo.networks. The AssociativeMemory SPA module also has an updated argument list. (#702)
  • The Product and InputGatedMemory networks no longer accept a config argument. (#814)
  • The EnsembleArray network's neuron_nodes argument is deprecated. Instead, call the new add_neuron_input or add_neuron_output methods. (#868)
  • The nengo.log utility function now takes a string level parameter to specify any logging level, instead of the old binary debug parameter. Cache messages are logged at DEBUG instead of INFO level. (#883)
  • Reorganised the Associative Memory code, including removing many extra parameters from nengo.networks.assoc_mem.AssociativeMemory and modifying the defaults of others. (#797)
  • Add close method to Simulator. Simulator can now be used used as a context manager. (`#857 <https://github.com/nengo/nengo/issues/857`_, #739, #859)
  • Most exceptions that Nengo can raise are now custom exception classes that can be found in the nengo.exceptions module. (#781)
  • All Nengo objects (Connection, Ensemble, Node, and Probe) now accept a label and seed argument if they didn't previously. (#958)

Behavioural changes

  • The sign on the PES learning rule's error has been flipped to conform with most learning rules, in which error is minimized. The error should be actual - target. (#642)
  • The PES rule's learning rate is invariant to the number of neurons in the presynaptic population. The effective speed of learning should now be unaffected by changes in the size of the presynaptic population. Existing learning networks may need to be updated; to achieve identical behavior, scale the learning rate by pre.n_neurons / 100. (#643)
  • The probeable attribute of all Nengo objects is now implemented as a property, rather than a configurable parameter. (#671)
  • Node functions receive x as a copied NumPy array (instead of a readonly view). (#716, #722)
  • The SPA Compare module produces a scalar output (instead of a specific vector). (#775, #782)
  • Bias nodes in spa.Cortical, and gate ensembles and connections in spa.Thalamus are now stored in the target modules. (#894, #906)

Improvements

  • Added a randomized_svd subsolver for the L2 solvers. This can be much quicker for large numbers of neurons or evaluation points. (#803)
  • Added PES.pre_tau attribute, which sets the time constant on a lowpass filter of the presynaptic activity. (#643)
  • EnsembleArray.add_output now accepts a list of functions to be computed by each ensemble. (#562, #580)
  • LinearFilter now has an analog argument which can be set through its constructor. Linear filters with digital coefficients can be specified by setting analog to False. (#819)
  • Added SqrtBeta distribution, which describes the distribution of semantic pointer elements. (#414, #430)
  • Added Triangle synapse, which filters with a triangular FIR filter. (#660)
  • Added utils.connection.eval_point_decoding function, which provides a connection's static decoding of a list of evaluation points. (#700)
  • Resetting the Simulator now resets all Processes, meaning the injected random signals and noise are identical between runs, unless the seed is changed (which can be done through Simulator.reset). (#582, #616, #652)
  • An exception is raised if SPA modules are not properly assigned to an SPA attribute. (#730, #791)
  • The Product network is now more accurate. (#651)
  • Numpy arrays can now be used as indices for slicing objects. (#754)
  • Config.configures now accepts multiple classes rather than just one. (#842)
  • Added add method to spa.Actions, which allows actions to be added after module has been initialized. (#861, #862)
  • Added SPA wrapper for circular convolution networks, spa.Bind (#849)
  • Added the Voja (Vector Oja) learning rule type, which updates an ensemble's encoders to fire selectively for its inputs. (see examples/learning/learn_associations.ipynb). (#727)
  • Added a clipped exponential distribution useful for thresholding, in particular in the AssociativeMemory. (#582)
  • Added a cosine similarity distribution, which is the distribution of the cosine of the angle between two random vectors. It is useful for setting intercepts, in particular when using the Voja learning rule. (#768)

Bug fixes

  • Fixed issue where setting Connection.seed through the constructor had no effect. (#724)
  • Fixed issue in which learning connections could not be sliced. (#632)
  • Fixed issue when probing scalar transforms. (#667, #671)
  • Fix for SPA actions that route to a module with multiple inputs. (#714)
  • Corrected the rmses values in BuiltConnection.solver_info when using NNls and Nnl2sL2 solvers, and the reg argument for Nnl2sL2. (#839)
  • spa.Vocabulary.create_pointer now respects the specified number of creation attempts, and returns the most dissimilar pointer if none can be found below the similarity threshold. (#817)
  • Probing a Connection's output now returns the output of that individual Connection, rather than the input to the Connection's post Ensemble. (#973, #974)

2.0.3 (December 7, 2015)

API changes

  • The spa.State object replaces the old spa.Memory and spa.Buffer. These old modules are deprecated and will be removed in 2.2. (#796)

2.0.2 (October 13, 2015)

2.0.2 is a bug fix release to ensure that Nengo continues to work with more recent versions of Jupyter (formerly known as the IPython notebook).

Behavioural changes

  • The IPython notebook progress bar has to be activated with %load_ext nengo.ipynb. (#693)

Improvements

  • Added [progress] section to nengorc which allows setting progress_bar and updater. (#693)

Bug fixes

  • Fix compatibility issues with newer versions of IPython, and Jupyter. (#693)

2.0.1 (January 27, 2015)

Behavioural changes

  • Node functions receive t as a float (instead of a NumPy scalar) and x as a readonly NumPy array (instead of a writeable array). (#626, #628)

Improvements

  • rasterplot works with 0 neurons, and generates much smaller PDFs. (#601)

Bug fixes

  • Fix compatibility with NumPy 1.6. (#627)

2.0.0 (January 15, 2015)

Initial release of Nengo 2.0! Supports Python 2.6+ and 3.3+. Thanks to all of the contributors for making this possible!