1. Quick Overview
1.1 The neuron itself
- Inputs are going to be binary
- Weighted sum is bias+W1*input1+w2*input2
- If weighted sum>=0 then the output is True (T on the LEDs) or '1'
- If weighted sum<0 then the output is False (F on the LEDs) or '0'
2. Neuron 1
This is the top left neurone in figure 1. This neurone is set to produce an output of TRUE (pin 2 going high) when the first input goes low and the second input goes high. The code for it is shown below.
3. Neuron 2
This is the bottom left neuron in figure 1. This neurone is set to produce an output of TRUE (pin 2 going high) when the first input goes high and the second input goes low. The code for it is shown below.
4. Output Neuron
This is the right-hand neurone in figure 1. This neurone is set to produce an output of TRUE (pin 2 going high) when either inputs (outputs from neurons 1 and 2) goes high - in other words acting as an OR gate . The code for it is shown below.
The overall effect is when the two inputs to the network are high/TRUE then the output of the network (this neuron) is TRUE.
5. In Action
The wiring is messy but the effect is possible to see in these images. The top neuron is the output neuron.
|figure 2: inputs to the network (input 1 low and input 2 high)|
|Figure 3: inputs to the network (input 1 high and input 2 low)|
|figure 4: inputs to the network (both inputs the same)|
The neurons were 'trained' in this case by selecting the weights by hand, an improvement would be to get them to learn. How to do this on a micro:bit takes a bit more thinking about, but I would be interested in seeing how others solve that problem.
All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon