1. Procedure for Training a Child to Identify a Cat using 10,000 Example Cats For Cat_index  1 to 10000 1. Show cat and describe catlike features (Cat_index) 2. Child adjusts biological neural network in response to receiving the features of example cat Cat_index 3. Cat_index  Cat_index + 1 Procedure for Testing a Trained Child’s ability to Identify a new Cat 1. Show new cat and describe catlike features 2. Child processes features with biological neural network in response to receiving the features of new example cat 3. Output of biological neural network indicates weather or not new example is a cat

2. Smoothing function for converting the output of a neuron into the range [0,1]

3. Forward Pass Computations through a Back-Propagation Neural Network with three layers having 4, 6, and 8 nodes INPUT input(1),input(2),input(3),input(4) For i  1 to 6 middle_in (i)  0 For j  1 to 4 middle_in (i) < middle_in (i) + weight(j,i) * inp8ut (j) middle_out (i)  Fermi (middle_in(i)) For k  1 to 8 output (k)  0 For i  1 to 6 output (k)  output (k) + weight (i,k) * middle_out (i) INPUT known_true_value (k) error (k)  known_true_value (k) – output (k)

4. General Procedure for training a neural network, then testing it on new examples INPUT known true values for each example For i  1 to number_of_examples_in_input_set INPUT numbers that measure values of input features for this example INPUT known true classification values for this example Do forward neural net computation to get outputs Compute error by subtracting known true values from outputs Set error_tolerance_threshold Repeat until error tolerance <= error_tolerance_threshold Do backpropagation for an epoch and adjust weights