Flow-field inference from neural data using deep recurrent networks.

Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity.

Theory of Gating in Recurrent Neural Networks.

Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive interactions. However gating i.

Spectral Gaps and Midgap States in Random Quantum Master Equations.

We discuss the decay rates of chaotic quantum systems coupled to noise. We model both the Hamiltonian and the system-noise coupling by random N×N Hermitian matrices, and study the spectral properties of the resulting Liouvillian superoperator. We consider various random-matrix ensembles, and find that for all of them the asymptotic decay rate remains nonzero in the thermodynamic limit; i.

Electromagnetic and gravitational responses of photonic Landau levels.

Topology has recently become a focus in condensed matter physics, arising in the context of the quantum Hall effect and topological insulators. In both of these cases, the topology of the system is defined through bulk properties ('topological invariants') but detected through surface properties. Here we measure three topological invariants of a quantum Hall material-photonic Landau levels in curved space-through local electromagnetic and gravitational responses of the bulk material.

Publisher's Note: Particle-Hole Duality in the Lowest Landau Level [Phys. Rev. Lett. 118, 206602 (2017)].

This corrects the article DOI: 10.1103/PhysRevLett.118.

Publisher's Note: Particle-Hole Duality in the Lowest Landau Level [Phys. Rev. Lett. 118, 206602 (2017)].

This corrects the article DOI: 10.1103/PhysRevLett.118.

Particle-Hole Duality in the Lowest Landau Level.

We derive a number of exact relations between response functions of holomorphic, chiral fractional quantum Hall states and their particle-hole (PH) conjugates. These exact relations allow one to calculate the Hall conductivity, Hall viscosity, various Berry phases, and the static structure factor of PH conjugate states from the corresponding properties of the original states. These relations establish a precise duality between chiral quantum Hall states and their PH conjugates.

Coherent transport and energy flow patterns in photosynthesis under incoherent excitation.

Long-lived coherences have been observed in photosynthetic complexes after laser excitation, inspiring new theories regarding the extreme quantum efficiency of photosynthetic energy transfer. Whether coherent (ballistic) transport occurs in nature and whether it improves photosynthetic efficiency remain topics of debate. Here, we use a nonequilibrium Green's function analysis to model exciton transport after excitation from an incoherent source (as opposed to coherent laser excitation).

Atomic resolution imaging of currents in nanoscopic quantum networks via scanning tunneling microscopy.

We propose a new method for atomic-scale imaging of spatial current patterns in nanoscopic quantum networks by using scanning tunneling microscopy (STM). By measuring the current flowing from the STM tip into one of the leads attached to the network as a function of tip position, one obtains an atomically resolved spatial image of "current riverbeds" whose spatial structure reflects the coherent flow of electrons out of equilibrium. We show that this method can be successfully applied in a variety of network topologies and is robust against dephasing effects.