Neural nets for cognition I recently got into Neural networks. As much as I have understood, the learning process is based on the change in weights according to stimulus and algorithm used in learning. Does this in any way represent the real learning process of our brain?
Yes and no.

Neural networks that use edge weights simulate synaptic plasticity - a key mechanism in the brain operation. But it isn't the only one.

In practice, cognition is the combination of various mechanisms, some we are still unsure of.

Perhaps the most obvious missing ingredient of weighted networks is the lack of frequency/time domains similar to that affecting the brain. Neurons involve time-dependent operation - for instance, both the action potential and the following refractory period are time-bound. The brain also involves synchrony, e.g. the phase-locked discharges of a neuron set.

There're also issues like different axon lengths that play a part. And many more.

In addition, and possibly above all, the actual topography matters a great deal - how everything is connected.

More advance brain simulations involve many of these extra components - mimicking more accurately a real biological brain.