the weights tell you what pixel pattern this neuron in the second layer is picking up on, and the bias tells you how big that weighted sum needs to be before the neuron gets meaningfully active.
The weights represent the strength of connections between each neuron in one layer and each neuron in the next. And each bias is an indication of whether its neuron tends to be active or inactive.