# Are neurons in layer $l$ only affected by neurons in the previous layer?

Are artificial neurons in layer $$l$$ only affected by those in layer $$l-1$$ (providing inputs) or are they also affected by neurons in layer $$l$$ (and maybe by neurons in other layers)?

It depends on the architecture of the neural network. However, in general, no, neurons at layer $$l$$ are not only affected by neurons at layer $$l-1$$.
In the case of a multi-layer perceptron (or feed-forward neural network), only neurons at layer $$l-1$$ directly affect the neurons at layer $$l$$. However, neurons at layers $$l-i$$, for $$i=2, \dots, l$$, also indirectly affect the neurons at layer $$l$$.
In the case of recurrent neural networks, the output of neuron $$j$$ at level $$l$$ can also affect the same neuron but at a different time step.
In the case of residual networks, the output of a neuron at a layer $$l-i$$, for $$i=2, \dots, l$$, can directly affect the neurons at layer $$l$$. These non-neighboring connections are called skip connections because they skip layers.