Search Results for « Continuous Dynamic Photostimulation »

Central neurons operate in a regime of constantly fluctuating conductances, induced by thousands of presynaptic cells. Channelrhodopsins have been almost exclusively used to imprint a fixed spike pattern by sequences of brief depolarizations. Here we introduce continuous dynamic photostimulation (CoDyPs), a novel approach to mimic in-vivo like input fluctuations noninvasively in cells transfected with the weakly inactivating channelrhodopsin variant ChIEF. Even during long-term experiments, cultured neurons subjected to CoDyPs generate seemingly random, but reproducible spike patterns. In voltage clamped cells CoDyPs induced highly reproducible current waveforms that could be precisely predicted from the light-conductance transfer function of ChIEF. CoDyPs can replace the conventional, flash-evoked imprinting of spike patterns in in-vivo and in-vitro studies, preserving natural activity. When combined with non-invasive spike-detection, CoDyPs allows the acquisition of order of magnitudes larger data sets than previously possible, for studies of dynamical response properties of many individual neurons.

We show how to construct Greenberger-Horn-Zeilinger type paradoxes for continuous variable systems. We give two examples corresponding to 3 party and 5 party paradoxes. The paradoxes are revealed by carrying out position and momentum measurements. The structure of the quantum states which lead to these paradoxes is discussed.

To help mitigate road congestion caused by the unrelenting growth of traffic demand, many transportation authorities have implemented managed lane policies, which restrict certain freeway lanes to certain types of vehicles. It was originally thought that managed lanes would improve the use of existing infrastructure through demand-management behaviors like carpooling, but implementations have often been characterized by unpredicted phenomena that are sometimes detrimental to system performance. The development of traffic models that can capture these sorts of behaviors is a key step for helping managed lanes deliver on their promised gains. Towards this goal, this paper presents an approach for solving for driver behavior of entering and exiting managed lanes at the macroscopic (i.e., fluid approximation of traffic) scale. Our method is inspired by recent work in extending a dynamic-system-based modeling framework from traffic behaviors on individual roads, to models at junctions, and can be considered a further extension of this dynamic-system paradigm to the route/lane choice problem. Unlike traditional route choice models that are often based on discrete-choice methods and often rely on computing and comparing drivers' estimated travel times from taking different routes, our method is agnostic to the particular choice of physical traffic model and is suited specifically towards making decisions at these interfaces using only local information. These features make it a natural drop-in component to extend existing dynamic traffic modeling methods.

Abstract This paper delves into the challenges of continuous path planning for robots in complex environments, addressing the limitations of traditional algorithms in handling sparse rewards and insufficient exploration.To tackle these issues, we propose an improved Deep Deterministic Policy Gradient (DDPG) algorithm named DDAPG, which integrates a potential energy-based reward reshaping method and an adaptive round Ornstein-Uhlenbeck (OU) noise model.The reward reshaping method enhances the algorithm's ability to provide smooth and continuous rewards, guiding the robot towards the target while avoiding obstacles effectively.Meanwhile, the adaptive OU noise model balances exploration and exploitation by adjusting the noise level according to the training progress, preventing the robot from getting stuck in local optima.Experimental results in a continuous simulation environment demonstrate that the DDAPG algorithm significantly improves convergence speed, reduces collision rates, and enhances adaptability compared to baseline algorithms.Specifically, DDAPG achieves a success rate of over 92% and a low collision rate of 7.625% in reaching target positions while navigating around obstacles.These findings underscore the potential of DDAPG in advancing embodied intelligence for robot path planning, offering a robust framework for deploying robots in real-world scenarios with dynamic and complex environments.

Mitochondrial research is important to ageing, apoptosis, and mitochondrial diseases. In previous works, mitochondria are usually stimulated indirectly by proapoptotic drugs to study mitochondrial development, which is in lack of controllability, or spatial and temporal resolution. These chemicals or even gene techniques regulating mitochondrial dynamics may also activate other inter- or intra-cellular processes simultaneously. Here we demonstrate a photostimulation method on single-mitochondrion level by tightly-focused femtosecond laser that can precisely activate restorable fragmentation of mitochondria which soon recover their original tubular structure after tens of seconds. In this process, series of mitochondrial reactive oxygen species (mROS) flashes are observed and found very critical to mitochondrial fragmentation. Meanwhile, transient openings of mitochondrial permeability transition pores (mPTP), suggested by oscillations of mitochondrial membrane potential, contribute to the scavenging of redundant mROS and recovery of fragmented mitochondria. Those results demonstrate photostimulation as an active, precise and controllable method for the study of mitochondrial oxidative and morphological dynamics or related fields.

Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users outside the zones from the interference caused by the deployed equipment within this zone. Previous works have utilized sparse reference signal received power (RSRP) measurements collected by unmanned aerial vehicles (UAVs) to construct a dense 3D radio map through ordinary Kriging. In this work, we illustrate that matrix completion can outperform ordinary Kriging. We partitioned a 2D area of interest into small square grids where each grid corresponds to a single entry of a matrix. The matrix completion algorithm learns the global structure of the radio environment map by leveraging the low-rank property of propagation maps. Additionally, we illustrate that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower. Earlier works of RSRP prediction involved a training dataset at a single altitude. In this work, we also show that performance can be improved by utilizing a combined dataset from multiple altitudes.

Protocol for photostimulation, to be combined with any other electrophysiology/imaging recording approach. Once the desired patch clamp recording (e.g., cell attached, whole cell, perforated patch) and/or imaging (e.g., genetically encoded sensor or synthetic dye) configuration is achieved and a stable recording is established, photostimulation can be used to stimulate presynaptic terminals/cell expressing an opsin of interest (e.g., Chronos or Channelrhodopsin) or to release a photo-sensitive caged compound (e.g., RUBI-GABA, RUBI-Glutamate, MNI-glutamate...), and test the effect on the experimental outcome of interest (e.g., excitatory/inhibitory response).