Of noise and neurons: Sensory coding, representation and short-term memory

While much is known about the limiting effect of neural noise on the fidelity of sensory coding representation, knowledge about the impact of noise in short-term memory and integrator networks has remained more elusive. (Integrator networks are networks of nodes – in this case neurons in a biological network – often recurrently connected, whose time dynamics settle to stable stationary, cyclic, or chaotic patterns, that can integrate or store memories of external inputs.)

Recently, however, scientists at The Hebrew University of Jerusalem, Harvard University and University of Texas, Austin used statistical and dynamical approaches to investigate how neural noise interacts with neural and network parameters to limit memory. They derived a series of unanticipated results – including the implications that short-term memory may be co-localized with sensory representation – by establishing a fundamental limit on the network’s ability to maintain a persistent neural state.