Owing to its many computationally desirable properties, the model of continuous attractor
neural networks (CANNs) has been successfully applied to describe the encoding of simple …

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 …

Continuous attractor models of working-memory store continuous-valued information in
continuous state-spaces, but are sensitive to noise processes that degrade memory …

Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only
their content, but also their temporal structure. The phenomenon of replay, in the …

Attractor networks require neuronal connections to be highly structured in order to maintain
attractor states that represent information, while excitation and inhibition balanced networks …

Hippocampal place cells in freely moving rodents display both theta phase precession and
procession, which is thought to play important roles in cognition, but the neural mechanism …

A range of perceptual and cognitive processes have been characterized from the
perspective of probabilistic representations and inference. To understand the neural circuit …

The brain performs probabilistic Bayesian inference to interpret the external world. The
sampling-based view assumes that the brain represents the stimulus posterior distribution …

How multiple sensory cues are integrated in neural circuitry remains a challenge. The
common hypothesis is that information integration might be accomplished in a dedicated …

In this paper we present a framework in which one can rigorously study the effect of spatio-
temporal noise on traveling waves, stationary patterns, and oscillations that are invariant …