Figure 5.

Multi-neuron Conditioning

(A) The Δf/f₀ activities N₁, N₂, and N₃ of three CNs were first transformed by a logistic function (see STAR Methods) into f(N₁), f(N₂), and f(N₃), and the ensemble activity computed by adding f(N₁) and f(N₂) and subtracting f(N₃). Each activity transform dictated the angular position of a robotic actuator and the ensemble activity was proportional to the effector distance to target.

(B) Example Δf/f₀ traces of the three simultaneously conditioned neurons, their ensemble activity computed as in (A), and its real-time transform into a rate signal of optical pulses encoding the distance to target.

(C) Mean and individual session (n = 8, two mice) target hit rates indicate that mice could learn the multi-neuron conditioning task under the guidance of optogenetic feedback.