670 research outputs found
Strong Effects of Network Architecture in the Entrainment of Coupled Oscillator Systems
Entrainment of randomly coupled oscillator networks by periodic external
forcing applied to a subset of elements is numerically and analytically
investigated. For a large class of interaction functions, we find that the
entrainment window with a tongue shape becomes exponentially narrow for
networks with higher hierarchical organization. However, the entrainment is
significantly facilitated if the networks are directionally biased, i.e.,
closer to the feedforward networks. Furthermore, we show that the networks with
high entrainment ability can be constructed by evolutionary optimization
processes. The neural network structure of the master clock of the circadian
rhythm in mammals is discussed from the viewpoint of our results.Comment: 15 pages, 11 figures, RevTe
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
A Robust Method for Detecting Interdependences: Application to Intracranially Recorded EEG
We present a measure for characterizing statistical relationships between two
time sequences. In contrast to commonly used measures like cross-correlations,
coherence and mutual information, the proposed measure is non-symmetric and
provides information about the direction of interdependence. It is closely
related to recent attempts to detect generalized synchronization. However, we
do not assume a strict functional relationship between the two time sequences
and try to define the measure so as to be robust against noise, and to detect
also weak interdependences. We apply our measure to intracranially recorded
electroencephalograms of patients suffering from severe epilepsies.Comment: 29 pages, 5 figures, paper accepted for publication in Physica
Chaos synchronization in networks of delay-coupled lasers: Role of the coupling phases
We derive rigorous conditions for the synchronization of all-optically
coupled lasers. In particular, we elucidate the role of the optical coupling
phases for synchronizability by systematically discussing all possible network
motifs containing two lasers with delayed coupling and feedback. Hereby we
explain previous experimental findings. Further, we study larger networks and
elaborate optimal conditions for chaos synchronization. We show that the
relative phases between lasers can be used to optimize the effective coupling
matrix.Comment: 21 pages, 10 figure
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Computational automation for efficient design of acoustic metamaterials
Acoustic metamaterials (AMMs) are an exciting technology because they are capable of responding to vibrations in ways that are impossible to achieve with conventional materials. However, realization of AMMs requires engineering design to provide a connection between first-principles research and production of parts that perform as expected. Designing AMMs is a challenging endeavor because evaluating designs is costly and manufacturing metamaterials requires precise techniques with small minimum resolutions. To address these challenges, new computational tools are necessary to aid design. This work proposes three tasks that improve the capabilities of design for AMM while being extensible to other engineering design automation tasks. The first task is to develop a design exploration tool that improves the computational efficiency of identifying sets of high-performing designs in a design space that is sparse and comprises mixed discrete/continuous data. The second task is to develop a process for designers to evaluate manufacturability of difficult-to-manufacture parts and drive co-development of manufacturing methods and AMM. In the final task, a machine learning based method is developed to efficiently model AMM with heterogeneous arrangements of their microstructures such that strict homogenization is infeasible. The outcomes from completing these tasks will provide a significant and novel improvement over existing methods of designing AMMs.Mechanical Engineerin
A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this paper, we propose an STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop, and find it shows preference for high-velocity objects. Extensive experiments suggest that the Feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening
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