Constructing functional models from biophysically-detailed neurons
Fig 6
Computing the identity function, Eq 11.
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 compute the identity function, such that “pop2” represents the same information as “pop1”. The top plot shows the state space target and the decoded estimates from “pop2”, and the bottom plot shows the mean error (RMSE) between this estimate and the target across 10 simulations with unique input signals.