A model of amacrine and ganglion cells for optical information processing

Based on a previously reported logic cell structure (see SPIE, vol. 2038, p. 67-77, 1993), the two types of cells present at the inner and ganglion cell layers of the vertebrate retina and their intracellular response, as well as their connections with each other, have been simulated. These cells are amacrines and ganglion cells. The main scheme of the authors' configuration is shown in a figure. These two types of cells, as well as some of their possible interconnections, have been implemented with the authors' previously reported optical-processing element. As it has been shown, the authors' logic structure is able to process two optical input binary signals, being the output two logical functions. Moreover, if a delayed feedback from one of the two possible outputs to one or both of the inputs is introduced, a very different behaviour is obtained. Depending on the value of the time delay, an oscillatory output can be obtained from a constant optical input signal. Period and length pulses are dependent on delay values, both external and internal, as well as on other control signals. Moreover, a chaotic behaviour can be obtained too under certain conditions

A model of the mammalian retina and the visual cortex. Disorders of vision

A model of the mammalian retina and the behavior of the first layers in the visual cortex is reported. The building blocks are optically programmable logic cells. A model of the retina, similar to the one reported by Dowling (1987) is presented. From the model of the visual cortex obtained, some types of symmetries and asymmetries are possible to be detected

Analysis of neuronal functions based on feynman diagrams

A method to study some neuronal functions, based on the use of the Feynman diagrams, employed in many-body theory, is reported. An equation obtained from the neuron cable theory is the basis for the method. The Green's function for this equation is obtained under some simple boundary conditions. An excitatory signal, with different conditions concerning high and pulse duration, is employed as input signal. Different responses have been obtained

Digital chaotic output from an optically processing element

Digital chaotic behavior in an optically processing element is reported. It is obtained as the result of processing two fixed trains of bits. The process is performed with an optically programmable logic gate, previously reported as a possible main block for optical computing. Outputs for some specific conditions of the circuit are given. Digital chaos is obtained using a feedback configuration. Period doublings in a Feigenbaum‐like scenario are obtained. A new method to characterize this type of digital chaos is reported.

Synchronizing chaotic optically-programmable digital circuits

In this paper an approach to the synchronization of chaotic circuits has been reported. It is based on an optically programmable logic cell and the signals involved are fully digital. It is based on the reception of the same input signal on sender and receiver and from this approach, with a posterior correlation between both outputs, an identical chaotic output is obtained in both systems. No conversion from analog to digital signals is needed. The model here presented is based on a computer simulation.

Panorámica global de la computación óptica. Una visión parcial

En el presente artículo se ofrece una visión panorámica de la situación actual de la Computación Óptica, dándose unas breves ideas de algunos de los temas fundamentales que abarca. Se pone especial énfasis en el aspecto de continuidad con tecnologías ya sobradamente conocidas desde hace varias décadas y se intenta pronosticar cuál será su posible futuro en los próximos años.

Chaos synchronization in optically programmable logic cells

A possible approach to the synchronization of chaotic circuits is reported. It is based on an Optically Programmable Logic Cell and the signals are fully digital. A method to study the characteristics of the obtained chaos is reported as well as a new technique to compare the obtained chaos from an emitter and a receiver. This technique allows the synchronization of chaotic signals. The signals received at the receiver, composed by the addition of information and chaotic signals, are compared with the chaos generated there and a pure information signal can be detected. Its application to cryptography in Optical Communications comes directly from these properties. The model here presented is based on a computer simulation.

Image processing based on a model of the mammalian retina

A first study in order to construct a simple model of the mammalian retina is reported. The basic elements for this model are Optical Programmable Logic Cells, OPLCs, previously employed as a functional element for Optical Computing. The same type of circuit simulates the five types of neurons present in the retina. Different responses are obtained by modifying either internal or external connections. Two types of behaviors are reported: symmetrical and non-symmetrical with respect to light position. Some other higher functions, as the possibility to differentiate between symmetric and non-symmetric light images, are performed by another simulation of the first layers of the visual cortex. The possibility to apply these models to image processing is reported.

Sequences of bifurcations and transition to chaos in an optical processing element

Digital chaotic behaviour in an Optical-Processing Element is reported. It is obtained as the result of processing two fixed trains of bits. Period doublings in a Feigenbaum-like scenario have been obtained. A new method to characterize digital chaos is reported

Digital chaos analysis in optical logic structures

Digital chaotic behavior in an optically processing element is analyzed. It was obtained as the result of processing two fixed trains of bits. The process is performed with an optically programmable logic gate. Possible outputs, for some specific conditions of the circuit, are given. Digital chaotic behavior is obtained, by using a feedback configuration. Different ways to analyze a digital chaotic signal are presented.

Use of Feynman diagrams in large-scale neural networks with nonlinear behaviour

A new proposal to the study of large-scale neural networks is reported. It is based on the use of similar graphs to the Feynman diagrams. A first general theory is presented and some interpretations are given. A propagator, based on the Green's function of the neuron, is the basis of the method. Application to a simple case is reported.

Quasi chaotic digital behaviour in an optically processing

Digital chaotic behavior in an optically processing element is reported. It is obtained as the result of processing two fixed train of bits. The process is performed with an Optically Programmable Logic Gate. Possible outputs for some specific conditions of the circuit are given. These outputs have some fractal characteristics, when input variations are considered. Digital chaotic behavior is obtained by using a feedback configuration. A random-like bit generator is presented.

Un análisis de la movilidad de los investigadores universitarios : caso "Electrical Engineering" en el mundo

En el presente trabajo se ha intentado obtener una visión, lo más exacta posible, de cuál ha sido el currículo académico seguido por los miembros de algunas de las universidades más prestigiosas en todo el mundo. Para ello se han tomado 36 centros, todos ellos impartiendo la misma titulación de "Electrical Engineering", situados entre los 50 más importantes del mundo.

Bit noise from an optical logic gate with laser diodes

Output bits from an optical logic cell present noise due to the type of technique used to obtain the Boolean functions of two input data bits. We have simulated the behavior of an optically programmable logic cell working with Fabry Perot-laser diodes of the same type employed in optical communications (1550nm) but working here as amplifiers. We will report in this paper a study of the bit noise generated from the optical non-linearity process allowing the Boolean function operation of two optical input data signals. Two types of optical logic cells will be analyzed. Firstly, a classical "on-off" behavior, with transmission operation of LD amplifier and, secondly, a more complicated configuration with two LD amplifiers, one working on transmission and the other one in reflection mode. This last configuration has nonlinear behavior emulating SEED-like properties. In both cases, depending on the value of a "1" input data signals to be processed, a different logic function can be obtained. Also a CW signal, known as control signal, may be apply to fix the type of logic function. The signal to noise ratio will be analyzed for different parameters, as wavelength signals and the hysteresis cycles regions associated to the device, in relation with the signals power level applied. With this study we will try to obtain a better understanding of the possible effects present on an optical logic gate with Laser Diodes.

Optical bistable devices as sensing elements

The nonlinear optical properties of many materials and devices have been the main object of research as potential candidates for sensing in different places. Just one of these properties has been, in most of the cases, the basis for the sensing operation. As a consequence, just one parameter can be detected. In this paper, although just one property will be employed too, we will show the possibility to sense different parameters with just one type of sensor. The way adopted in this work is the use of the optical bistability obtained from different photonic structures. Because this optical bistability has a strong dependence on many different parameters the possibility to sense different inputs appears. In our case, we will report the use of some non-linear optical devices, mainly Semiconductor Optical Amplifiers, as sensing elements. Because their outputs depend on many parameters, as the incident light wavelength, polarization, intensity and direction, applied voltage and feedback characteristics, they can be employed to detect, at the same time, different type of signals. This is because the way these different signals affect to the sensor response is very different too and appears under a different set of characteristics.