In this tutorial, you will connect groups of neurons to create a system that can multiply two numbers together. You will then modify this network to compute a new function.
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Open the partially completed multiplication network
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Go to File->Open from file and select partial multiplication.nef.
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Connect the network
- The basic components of the network have been constructed for you. Connecting components tells the system to compute the necessary synaptic connection weights to optimally realize the desired function.
- Connect input 1 and input 2 to the inputs for neural ensembles A and B.
- Connect A and B to the two inputs of H.
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Connect the product output of H to the input of Z.
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Run the network
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Right-click on the background in the Network Viewer and select Interactive plots.
- Click the play button (in the bottom-right corner). The grey squares show the firing rates of the neurons.
- The graph displays the decoded output of the network. This should be approximately 40, since the input sliders are currently set to 8 and 5.
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Move the sliders up and down to adjust this input. The output will change accordingly.
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Change the function being computed
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Close the Interactive Plots window.
- Right-click on the H population and select Add decoded origin. Change the name to
my function
and click Set functions (dimension should be 1). Select user-defined function from the drop down and click set. - Type in a new function. Try
x0*x0+cos(x1)
. You can also use your own function, but the neurons in this example are only optimized for representing values between -100 and 100. Click OK three times. - Disconnect the product projection from H to Z and connect the new origin called my function in its place.
- Right-click on the Network Viewer and select Interactive plots.
- Adjust the sliders to confirm that the new function is being calculated.
- To compare the behaviour of neurons to the ideal calculation of this function, we can switch simulation modes. Click on the down-arrow at the bottom of the interactive plots. Change the mode from default to direct. This will bypass the neurons, producing an exact result.