**Today I learned…**

…how neural networks (think brains) can do differentiation by using temporal inhibition – i.e. by using a delayed signal. In the figure below, the node **α** will send a signal to two nodes. One of them – **β** – will pass on an inhibitory signal of the same strength as its input signal, but with a delay. Thus, when **β**’s signal gets sent to the final node, **α** will at the same time be sending its “next” output signal to the final node.

Therefore, the final node will receive two signals: the current output of **α** and the inverted previous output of **α**. If the final node sums these together its output will therefore be **α**’s current value minus its old value – i.e. positive if **α**’s output signal is increasing and negative if it is decreasing. Simple and beautiful!