function NeuralNetDecisionBoundaryPlot(syn_wts,plot_symbol_str,bounding_box)
% Plot decision boundaries of two-input (plus threshold) neurons.
%
% Decision boundary = line where sum of wts * inputs = 0.
% syn_wts = 3 cols for w0, w1, w2 and one row for each neuron.
% plot_symbol_str = 'r-' (red line) or similar string for plot command.
% bounding_box = [x1min, x1max, x2min, x2max]

% Extract bounding box parameters for clarity.
x1min = bounding_box(1);
x1max = bounding_box(2);
x2min = bounding_box(3);
x2max = bounding_box(4);

% Plot the x1 and x2 axes in black inside bounding box.
plot([x1min, x1max],[0,0],'k-')
hold on
plot([0,0],[x2min, x2max],'k-')
axis(bounding_box)

% Step through neurons.
for neuron_index = 1:size(syn_wts,1)
  % Extract wts for this neuron, for convenience and clarity.
  w0 = syn_wts(neuron_index,1);
  w1 = syn_wts(neuron_index,2);
  w2 = syn_wts(neuron_index,3);

  % Determine endpoints of decision boundary intersect bounding box.
  if abs(w2) < 10*eps
    % vertical decision boundary, solve for x1 values at endpts.
    x1_left = -w0/w1;
    x2_left = x2min;
    x1_right = -w0/w1;
    x2_right = x2max;
  else
    x1_left = x1min;
    x2_left = (-w0 -w1*x1min)/w2;
    x1_right = x1max;
    x2_right = (-w0 -w1*x1max)/w2;
  end
  plot([x1_left,x1_right],[x2_left,x2_right],plot_symbol_str)
end

hold off

return