Why do we need target network in deep Q learning? [duplicate]

I already know deep RL, but to learn it deeply I want to know why do we need 2 networks in deep RL. What does the target network do? I now there is huge mathematics into this, but I want to know deep Q-learning deeply, because I am about to make some changes in the deep Q-learning algorithm (i.e. invent a new one). Can you help me to understand what happens during executing a deep Q-learning algorithm intuitively?

In DQN that was presented in the original paper the update target for the Q-Network is $$\left(r_t + \max_aQ(s_{t+1},a;\theta^-) - Q(s_t,a_t; \theta)\right)^2$$ were $$\theta^-$$ is some old version of the parameters that gets updated every $$C$$ updates, and the Q-Network with these parameters is the target network.
If you didn't use this target network, i.e. if your update target was $$\left(r_t + \max_aQ(s_{t+1},a;\theta) - Q(s_t,a_t; \theta)\right)^2$$, then learning would become unstable because the target, $$r_t + \max_aQ(s_{t+1},a;\theta)$$, and the prediction, $$Q(s_t,a_t; \theta)$$, are not independent, as they both rely on $$\theta$$.