No, we still have two filters in each layer. Each filter calculates the dot product in the input activation using all input channels.
Have a look at the alexnet architecture 13 in Figure 2. You see that each filter has a depth in the input volume.
Also, have a look at the Convolution lecture of CS231n 12.
Some information:

The connections are local in space (along width and height), but always full along the entire depth of the input volume. For example, suppose that the input volume has size [32x32x3], (e.g. an RGB CIFAR-10 image). If the receptive field (or the filter size) is 5x5, then each neuron in the Conv Layer will have weights to a [5x5x3] region in the input volume, for a total of 553 = 75 weights (and +1 bias parameter). Notice that the extent of the connectivity along the depth axis must be 3, since this is the depth of the input volume.