Neural Integrator Models

Integration of information across time is a neural computation of critical importance to a variety of brain functions. Examples include oculomotor neural integrators and head direction cells that integrate velocity signals into positional or directional signals, parametric working memory circuits which convert transient input pulses into self-sustained persistent neural activity patterns, and linear ramping neural activity underlying the accumulation of information during decision making. How is integration over long timescales realized in neural circuits? This article reviews experimental and theoretical work related to this fundamental question, with a focus on the idea that recurrent synaptic or cellular mechanisms can instantiate an integration time much longer than intrinsic biophysical time constants of the system. We first introduce some basic concepts and present two types of codes used by neural integrators - the location code and the rate code. Then we summarize models that implement a variety of candidate mechanisms for neural integration in the brain, and we discuss the problem of fine-tuning of model parameters and possible solutions to this problem. Finally, we outline challenges for future research.