Principles of real-time computing with feedback applied to cortical microcircuit models.
The network topology of neurons in the brain exhibits an abundance of feedback connections, but the computational function of these feedback connections is largely unknown. We present a computational theory that characterizes the gain in computational power achieved through feedback in dynamical systems with fading memory. In particular, we show that feedback enables such systems to process time-varying input streams in dependence of internal states that may change on a much slower time scale. In contrast to previous attractor-based computational models for neural networks, these flexible internal states represent just partial attractors for the circuit dynamics, since they still allow the circuit state to respond to new inputs. Since the resulting computational model is noise robust, it can be applied to cortical microcircuit models with high levels of noise that reflect experimental data on in-vivo conditions.