All releases of SigmaPi Neurosimulator

  •  04 Jan 2003 22:51
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    Release Notes: This version introduces early stopping and colour-encoded error displays. It uses double-precision floating point numbers like version 0.1 and 0.2. Some bugs were also fixed.

    •  30 Dec 2002 18:40
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      Release Notes: A bug in the WAV/ASCII save procedure has been fixed.

      •  27 Dec 2002 03:14
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        Release Notes: This one can read and write Ascii and WAV files, some bugs were removed, and the interface is more comfortable.

        •  13 Oct 2002 13:50
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          Release Notes: An option to connect neurons with different connection types simultaneously was added. An option to upload algorithm settings from a file directly to a selected connection was added. A statusbar was added.

          •  24 Jul 2002 17:50
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            Release Notes: This version includes algorithm save/load functionality plus two new activation functions: the 'Mexican Hat' and the 'Gaussian Derivative'.

            •  27 Jun 2002 15:59
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              Release Notes: A momentum parameter has been introduced to speed up the training algorithm. Furthermore, a new update option has been introduced to discard potentially harmful updates.

              •  22 Jun 2002 18:42
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                Release Notes: This version adds the option to use the hyperbolic tangents or the Gaussian bell for gate activation.

                •  15 Jun 2002 17:33
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                  Release Notes: This version is able to process more than one set of signals. Every set of signals is treated as a different pattern. All patterns where at least one target signal is provided can be trained. Furthermore, RTRL now allows post-update gradient recalculations to improve precision.

                  •  22 May 2002 16:57
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                    Release Notes: The LSTM neuron model has been extended to 'Forget Gates'.

                    •  14 May 2002 20:41
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                      Release Notes: This release has been extended to LSTM (Long Short-Term Memory) neurons to overcome RTRL limitations. It is about seven times faster than 0.4.

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