Homepage of Louis Coder (Matthias Mueller)
Main page > Louis' Program to build and test (kind of) Neural Circuits

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Neuron Connection IDE

- Build and test neural circuits -


./NCIDE 1.0 Beta 2.zip

.ncide Files for Beta 2 (contain circuits for various purposes, download to disk and press F1 in running NCIDE to load from disk):

- no additional files yet, but there are many sample .ncide files included in "./NCIDE 1.0 Beta 2.zip" (downloadable via the link above!).

Installation as in the following video:

Run NCIDE and press F9 on your keyboard for a short summary about the purpose, the usage, and the basic working mechanisms of the program.

More to come soon...



./NCIDE 1.0 Beta 1.zip
[obsolete, please don't use any more,
as .ncide file format changed in Beta 2
and later]
[revised on 2022-08-16, for archiving]

.ncide Files for Beta 1 (ATTENTION: cannot be loaded by Beta 2 and later anymore, please use at least Beta 2 for saving your own circuits!):

Test.SuccessionRecognition.ncide - [obsolete] Detect succession of words just by AND-ing random points from axons of these words.

Test.WillToSpeak_'say hello'.ncide - [obsolete] 'The opposite' of Test.SuccessionRecognition.ncide - I think there's a uniformity in the brain, here you can see that it could work like this, long axon pathways (signal propagation times matter!) with chaotically connected dendrites of neurons which (logically) AND their input.

- [obsolete] Attempt to associate a retina image with output of some neuron by increasing neuron input weights (as done in computer science) - doesn't work right, success depends on right duration of the excitements (the longer, the higher the weight increase), the timing (input nodes must be excited at the same time to increase weight) and the count of input nodes - not good.

- [obsolete] Second attempt - there's at least one neuron which recognizes the image FROM BIRTH because it is "by chance" connected with the "right" pixels (i. e. retina cells) - in reality, there must of course be layers of those neurons which recognize sub-parts of the retina image (pyramidal), because the count of possible retina images is too high to be detected each by one single neuron. This attempt works well. Notice, in this attempt, also fixed images could be detected from birth (to detect dangers like predators etc.), I think it is improbable that evolution developed a completely new mechanism for learning images - just the output of the neuron related to the image needs to be newly connected.

- [obsolete] Like the previous test, but detects "happy/neutral smiley" with the SAME neuron no matter where on retina. Another set of axon branches leads to a simple detected WHERE something is seen (here just where enough retina cells are activated). Making the content recognition independent of the size or angle of the retina image could work accordingly.

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