Fourier Decomposition Analysis af Anisotropic Inhomogeneous Dielectric Waveguide Structures

In this paper we extend the Fourier decomposition method to compute both propagation constants and the corresponding electromagnetic field distributions of guided waves in millimeter-wave and integrated optical structures. Our approach is based on field Fourier expansions of a pair of wave equations which have been derived to handle inhomogeneous mediums with diagonalized permittivity and permeability tensors. The tensors are represented either by a grid of homogeneous rectangles or by distribution functions defined over rectangular domains. Using the Fourier expansion, partial differential equations are converted to a matrix eigenvalue problem that correctly models this class of dielectric structures. Finally numerical results are presented for various channel waveguides and are compared with those of other literatures to validate our formulation

Realization of Receptive Fields with Excitatory and Inhibitory Responses on Equilibrium-State Luminescence of Electron Trapping Material Thin Film

Our theoretical modelings and experimental observations illustrate that the equilibrium-state luminescence of electron-trapping materials (ETMs) can be controlled to produce either excitatory or inhibitory responses to the same optical stimulus. Because of this property, ETMs have a unique potential in optical realization of neurobiologically based parallel computations. As a classic example, we have controlled the equilibrium-state luminescence of a thin film of this stimulable storage phosphor to make it behave similarly to the receptive fields of sensory neurons in the mammalian visual system, which are responsible for early visual processing

Dynamics of electron-trapping materials under blue light and near infrared exposure: an improved model

Dynamics of electron-trapping materials (ETMs) is investigated. Based on experimental observations, evolution of the ETM\u27s luminescence is mathematically modeled by a nonlinear differential equation. This improved model can predict dynamics of ETM under blue light and near-infrared (NIR) exposures during charging, discharging, simultaneous illumination, and in the equilibrium state. The equilibrium-state luminescence of ETM is used to realize a highly nonlinear optical device with potential applications in nonlinear optical signal processing

Optical Realization of the Retinal Ganglion Receptive Fields in Electron-Trapping Material Thin Film

Optical control of the electron-trapping material is used to model the retinal ganglion cell’s receptive field. Using this approach all the retinal image processing can be done on the surface of a thin film of this material

Optical Realization of Bio-inspired Spiking Neurons In Electron Trapping Material Thin

A thin film of electron-trapping material (ETM), when combined with suitable optical bistability, is considered as medium for optical implementation of bio-inspired neural nets. The optical mechanism of ETM under blue light and NIR exposure has the inherent ability at the material level to mimic the crucial components of the stylized Hodgkin-Huxley model of biological neuron. Combining this unique property with high resolution capability of ETM, a dense network of bio-inspired neurons can be realized in a thin film of this infrared stimulable storage phosphore. The work presented here, when combined with suitable optical bistability and optical interconnectivity, has the potential of producing an artificial nonlinear excitable medium analogue to cortical tissue

Self-Organization in a Parametrically Coupled Logistic Map Network: A Model for Information Processing in the Visual Cortex

In this paper, a new model seeking to emulate the way the visual cortex processes information and interacts with subcortical areas to produce higher level brain functions is described. We developed a macroscopic approach that incorporates salient attributes of the cortex based on combining tools of nonlinear dynamics, information theory, and the known organizational and anatomical features of cortex. Justifications for this approach and demonstration of its effectiveness are presented. We also demonstrate certain capabilities of this model in producing efficient sparse representations and providing the cortical computational maps

An analytic model for the dynamics of electron trapping materials with applications in nonlinear optical signal processing

In this paper the optical mechanism and dynamics of electron trapping material under simultaneous illumination with two wavelengths is investigated. Our analytical model proves that the equilibrium state luminescence of such a material can be controlled to produce highly nonlinear behavior with potential applications in nonlinear optical signal processing and optical realization of nonlinear dynamical systems. Combining this new approach with state-of-the-art fast spatial light modulators and CCD cameras that can precisely control and measure exposure, large arrays of nonlinear processing elements can be accommodated in a thin film of this material

Optogenetic Brain Interfaces

The brain is a large network of interconnected neurons where each cell functions as a nonlinear processing element. Unraveling the mysteries of information processing in the complex networks of the brain requires versatile neurostimulation and imaging techniques. Optogenetics is a new stimulation method which allows the activity of neurons to be modulated by light. For this purpose, the cell-types of interest are genetically targeted to produce light-sensitive proteins. Once these proteins are expressed, neural activity can be controlled by exposing the cells to light of appropriate wavelengths. Optogenetics provides a unique combination of features, including multimodal control over neural function and genetic targeting of specific cell-types. Together, these versatile features combine to a powerful experimental approach, suitable for the study of the circuitry of psychiatric and neurological disorders. The advent of optogenetics was followed by extensive research aimed to produce new lines of light-sensitive proteins and to develop new technologies: for example, to control the distribution of light inside the brain tissue or to combine optogenetics with other modalities including electrophysiology, electrocorticography, nonlinear microscopy, and functional magnetic resonance imaging. In this paper, the authors review some of the recent advances in the field of optogenetics and related technologies and provide their vision for the future of the field.United States. Defense Advanced Research Projects Agency (Space and Naval Warfare Systems Center, Pacific Grant/Contract No. N66001-12-C-4025)University of Wisconsin--Madison (Research growth initiative; grant 101X254)University of Wisconsin--Madison (Research growth initiative; grant 101X172)University of Wisconsin--Madison (Research growth initiative; grant 101X213)National Science Foundation (U.S.) (MRSEC DMR-0819762)National Science Foundation (U.S.) (NSF CAREER CBET-1253890)National Institutes of Health (U.S.) (NIH/NIBIB R00 Award (4R00EB008738)National Institutes of Health (U.S.) (NIH Director’s New Innovator award (1-DP2-OD002989))Okawa Foundation (Research Grant Award)National Institutes of Health (U.S.) (NIH Director’s New Innovator Award (1DP2OD007265))National Science Foundation (U.S.) (NSF CAREER Award (1056008)Alfred P. Sloan Foundation (Fellowship)Human Frontier Science Program (Strasbourg, France) (Grant No. 1351/12)Israeli Centers of Research Excellence (I-CORE grant, program 51/11)MINERVA Foundation (Germany

Modeling and optoelectronic realization of an artificial cortex

Cortex, the outermost layer of the cerebrum, is recognized as the most developed part of the brain. It is believed that the higher-level functionality of the brain, the operations such as perception, cognition, and learning of both static and dynamic sensory information, originates from the dynamics of the massively interconnected gray cells of cortex. Because of the compact three-dimensional architecture of this biological computational paradigm, realization of bio-inspired machines that imitate such functionalities, including all the cellular details, is prohibitively difficult even if we consider the available nano-fabrication technologies. Based on this logical deduction, instead of considering each single neuron, an intriguing conjecture is to build aggregate level models that mimic the behavior of a population of neurons with collective emergent properties. In our approach, which is presented in this dissertation, cortex is assumed to be a composition of a sequence of discernable interconnected cortical patches. Each concerned patch is a network of asymmetrically coupled complex processing elements whose dynamics contain not only fixed-point and periodic attractors but also bifurcation and chaos. Dynamics of the complex processing elements, in this dissertation, is mathematically modeled by a slight modification of the time evolution of netlets adapted from computational neuroscience. Regarding this modification, the dynamics of a netlet is approximated by that of a quadratic return map. Studying the previous experimental observations demonstrates that a smart way of coupling such processing units is to couple them through their bifurcation parameters. Putting all pieces of this puzzle together, we model each cortical patch by a network of parametrically coupled quadratic return maps. Our simulations prove the ability of this network to emulate many salient features of cortical information processing, such as clustering, classification, generation of sparse codes and cortical topological computational maps. Next step in this research is seeking suitable enabling technologies, such as electronics and optics, for hardware implementation of these cortical models. It is a general consensus that realization of parallelism and massive interconnections can be done far better in optics compared to electronics. Nevertheless, one can exploit optoelectronic methodologies that combine the benefits of optics with flexibilities of electronics. An innovative optoelectronic approach is taking advantage of the optical mechanism of a special type of stimulable storage phosphor, the so called electron trapping materials. Our analytical modelings and experimental works reveal that the equilibrium state luminescence of this material can be controlled to generate a variety of different nonlinear behaviors including quasi-quadratic responses that can be used for generation of quadratic return maps. Combining this versatility with the state-of-the-art high speed spatial light modulators and CCD cameras, large arrays of quadratic return maps can be accommodated in a thin film of electron trapping material. Another approach which is investigated in this dissertation is based on using the recently developed digital microelectromechanic spatial light modulators. These modulators can control the exposure precisely. We show that a closed loop of such a spatial light modulator and a CCD camera can be used to build an optoelectronic machine suitable for parallel recursive computations similar to our cortical models