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Release History

0.4.3 (unreleased)

Added

  • Added the nengolib.RLS() recursive least-squares (RLS) learning rule. This can be substituted for nengo.PES(). See notebooks/examples/full_force_learning.ipynb for an example that uses this to implement spiking FORCE in Nengo. (#133)
  • Added the nengolib.stats.Rd() method for quasi-random sampling of arbitrarily high-dimensional vectors. It is now the default method for scattered sampling of encoders and evaluation points. The method can be manually switched back to nengolib.stats.Sobol(). (#153)

0.4.2 (May 18, 2018)

Tested against Nengo versions 2.1.0-2.7.0.

Added

  • Solving for connection weights by accounting for the neural dynamics. To use, pass in nengolib.Temporal() to nengo.Connection for the solver parameter. Requires nengo>=2.5.0. (#137)

0.4.1 (December 5, 2017)

Tested against Nengo versions 2.1.0-2.6.0.

Fixed

  • Compatible with newest SciPy release (1.0.0). (#130)

0.4.0b (June 7, 2017)

Initial beta release of nengolib. Tested against Nengo versions 2.1.0-2.4.0.