arXiv subjects

Jimin Kim

Publications and source records attributed to Jimin Kim.

23 records · Page 2Linked to original sources

arXiv2020

Deep Reinforcement Learning for Neural Control

We present a novel methodology for control of neural circuits based on deep reinforcement learning. Our approach achieves aimed behavior by generating external continuous stimulation of existing neural circuits (neuromodulation control) or modulations of neural circuits architecture (connectome control). Both forms of control are challenging due to nonlinear and recurrent complexity of neural activity. To infer candidate control policies, our approach maps neural circuits and their connectome into a grid-world like setting and infers the actions needed to achieve aimed behavior. The actions are inferred by adaptation of deep Q-learning methods known for their robust performance in navigating grid-worlds. We apply our approach to the model of \textit{C. elegans} which simulates the full somatic nervous system with muscles and body. Our framework successfully infers neuropeptidic currents and synaptic architectures for control of chemotaxis. Our findings are consistent with in vivo measurements and provide additional insights into neural control of chemotaxis. We further demonstrate the generality and scalability of our methods by inferring chemotactic neural circuits from scratch.

q-bio.NC↗

arXiv2017

Universal Mechanism of Band-Gap Engineering in Transition-Metal Dichalcogenides

Two-dimensional (2D) van-der-Waals semiconductors have emerged as a class of materials with promising device characteristics owing to the intrinsic bandgap. For realistic applications, the ideal is to modify the bandgap in a controlled manner by a mechanism that can be generally applied to this class of materials. Here, we report the observation of a universally tunable bandgap in the family of bulk 2H transition metal dichalcogenides (TMDs) by in situ surface doping of Rb atoms. A series of angle-resolved photoemission spectra unexceptionally shows that the bandgap of TMDs at the zone corners is modulated in the range of 0.8 ~ 2.0 eV, which covers a wide spectral range from visible to near infrared, with a tendency from indirect to direct bandgap. A key clue to understand the mechanism of this bandgap engineering is provided by the spectroscopic signature of symmetry breaking and resultant spin splitting, which can be explained by the formation of 2D electric dipole layers within the surface bilayer of TMDs. Our results establish the surface Stark effect as a universal mechanism of bandgap engineering based on the strong 2D nature of van-der-Waals semiconductors.

cond-mat.mtrl-sci↗

arXiv2017

Two-Dimensional Dirac Fermions Protected by Space-Time Inversion Symmetry in Black Phosphorus

We report the realization of novel symmetry-protected Dirac fermions in a surface-doped two-dimensional (2D) semiconductor, black phosphorus. The widely tunable band gap of black phosphorus by the surface Stark effect is employed to achieve a surprisingly large band inversion up to ~0.6 eV. High-resolution angle-resolved photoemission spectra directly reveal the pair creation of Dirac points and their moving along the axis of the glide-mirror symmetry. Unlike graphene, the Dirac point of black phosphorus is stable, as protected by spacetime inversion symmetry, even in the presence of spin-orbit coupling. Our results establish black phosphorus in the inverted regime as a simple model system of 2D symmetry-protected (topological) Dirac semimetals, offering an unprecedented opportunity for the discovery of 2D Weyl semimetals.

cond-mat.mtrl-sci↗

arXiv2017

Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans

We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response pathways in dynamic neuronal networks. In particular, posterior inference reveals the relations between known stimuli and downstream neurons or allows to query which stimuli are associated with downstream neurons. For validation and as an example for our approach we apply our methodology to a model of Caenorhabiditis elegans nervous system which structure and dynamics are well-studied. From its dynamical model we collect time series of the network response and use singular value decomposition to obtain a low-dimensional projection of the time series data. We then extract dominant patterns in each data matrix to get pairwise dependency information and create a graphical model for the full somatic nervous system. The PGM enables us to obtain and verify underlying neuronal pathways dominant for known behavioral scenarios and to detect possible pathways for novel scenarios.

q-bio.NC↗

arXiv2015

Observation of tunable bandgap and anisotropic Dirac semimetal state in black phosphorus

Black phosphorus consists of stacked layers of phosphorene, a two-dimensional semiconductor with promising device characteristics. We report the realization of a widely tunable bandgap in few-layer black phosphorus doped with potassium using an in-situ surface doping technique. Through band-structure measurements and calculations, we demonstrate that a vertical electric field from dopants modulates the bandgap owing to the giant Stark effect and tunes the material from a moderate-gap semiconductor to a band-inverted semimetal. At the critical field of this band inversion, the material becomes a Dirac semimetal with anisotropic dispersion, linear in armchair and quadratic in zigzag directions. The tunable band structure of black phosphorus may allow great flexibility in design and optimization of electronic and optoelectronic devices.

cond-mat.mtrl-sci↗