AND, OR, XOR Working

Check commented values to change operator

Author: FinHerbig

and_net.py

@@ -73,17 +73,20 @@ def run_net(self, input, time = run_time):
         # Initializes a current for the three input neurons
         xIn = self.spikes_to_current(self.binary_to_spikes(input[0]), self.connections[0].weight)
         yIn = self.spikes_to_current(self.binary_to_spikes(input[1]), self.connections[1].weight)
-        # Change and to xor/or
         #teachIn = self.spikes_to_current(self.binary_to_spikes(input[1] and input[0]), self.connections[2].weight)
         self.neurons[0].sim(xIn, time)
         self.neurons[1].sim(yIn, time)
         #self.neurons[2].sim(teachIn, time)
         # Calculates the input current for the output by using an or function on all 3 spike outputs 
         outIn = []
         for i in range(time):
-            if(self.neurons[0].output[i] == 1 and self.neurons[1].output[i] == 1):
+            #Change to and, or, ^
+            if(self.neurons[0].output[i] ^ self.neurons[1].output[i]):
                 outIn.append(1)
             else:
                 outIn.append(0)
         #Conversion factor scales # of spikes from output
-        self.neurons[3].sim(self.spikes_to_current(outIn, .7), time)
+        #AND: .7
+        #OR: .06
+        #XOR: .6
+        self.neurons[3].sim(self.spikes_to_current(outIn, .6), time)

data.txt

@@ -94,7 +94,7 @@
 01
 10
 11
-10
+00
 10
 11
-10
+00

training.py

@@ -4,6 +4,9 @@
 net = and_net([], [])
 net.create_simple_net()
 # X at [0], Y at [1], Teacher at [2]
+# AND: .6, .4
+# OR: .5, .5
+# XOR: .6, .4
 net.connections[0].weight = .6
 net.connections[1].weight = .4
 net.connections[2].weight = 1
@@ -95,7 +98,8 @@ def raster_plot(net):
     x_spikes = sum(net.neurons[0].output)
     y_spikes = sum(net.neurons[1].output)
     o_spikes = sum(net.neurons[3].output)
-    print((data[r][0] and data[r][1]), "||", net.spikes_to_binary(net.neurons[3]), "\nX =", 
+    #Change operator to and, or, ^
+    print((data[r][0] ^ data[r][1]), "||", net.spikes_to_binary(net.neurons[3]), "\nX =", 
     net.connections[0].weight, x_spikes, "\nY =", net.connections[1].weight, y_spikes, "\n", o_spikes, "\n")
     for i in range(4):
         net.neurons[i].clear()