Fundamental limits on persistent activity in networks of noisy neurons.

ABSTRACT: Neural noise limits the fidelity of representations in the brain. This limitation has been extensively analyzed for sensory coding. However, in short-term memory and integrator networks, where noise accumulates and can play an even more prominent role, much less is known about how neural noise interacts with neural and network parameters to determine the accuracy of the computation. Here we analytically derive how the stored memory in continuous attractor networks of probabilistically spiking neurons will degrade over time through diffusion. By combining statistical and dynamical approaches, we establish a fundamental limit on the network's ability to maintain a persistent state: The noise-induced drift of the memory state over time within the network is strictly lower-bounded by the accur

SUBMITTER: Burak Y 

PROVIDER: S-EPMC3491496 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature