Computing the product of two unique input signals, Eq 12.

Using the network architecture in Fig 5, we initialize neural populations “pop1” and “pop2” with 100 detailed neurons, then use osNEF to train encoders, decoders, and synaptic filters. The connection between “pop1” and “pop2” is trained to multiply two scalars: “pop1” represents the two scalars, and “pop2” should represent their product. The top plot shows the state space target and the decoded estimates from “pop2”, and the bottom plot shows the mean error between this estimate and the target across 10 simulations with unique input signals.