NEURONAL DYNAMICS I:ACTIVATIONS AND SIGNALS SIGNAL MONOTONICITY neurons are nonlinear but not too much so----a property as semilinearity Linear signal functions make computation and analysis comparatively easy - do not suppress noise linear network are not robust Nonlinear signal functions increases a network's computational richness increases a network's facilitates noise suppression risks computational and analytical intractability favors dynamical instability 88 SIGNAL MONOTONICITY neurons are nonlinear but not too much so ---- a property as semilinearity Linear signal functions - make computation and analysis comparatively easy - do not suppress noise - linear network are not robust Nonlinear signal functions - increases a network’s computational richness - increases a network’s facilitates noise suppression - risks computational and analytical intractability - favors dynamical instability NEURONAL DYNAMICS Ⅰ: ACTIVATIONS AND SIGNALS