Supplementary Information 10 Relation between low pass filter and

Eqn. 3 represents the solution for a stationary energy with respect to the prospective voltage ŭ Here we consider a generalization of the energy function from the main manuscript that includes arbitrary "connectivity functions" f with parameters θ: E(ŭ Pseudo-code for our vanilla implementation can be found in Algorithm 1. Algorithm 1 Pseudo-code for the multi-layer implementation of Latent Equilibrium (LE) Figure 5: Learning to mimic a teacher microcircuit with LE. For the interneurons, the somatic membrane potentials of the pyramidal neurons in the layer above serve as targets. First, the output rate of the neurons must depend on the prospective voltage: ϕ (u) ϕ (ŭ). Note that this includes also the rates in the calculation of dendritic membrane potentials (Eqns. Learning is split into two stages: first, the learning of the so-called self-predicting state and afterwards the learning of the actual task.