The mammalian visual system is endowed with a nearly infinite capacity for the recognition of patterns and objects. To have acquired this capability the visual system must have solved what is a fundamentally combinatorial prob­ lem. Any given image consists of a collection of features, consisting of local contrast borders of luminance and wavelength, distributed across the visual field. For one to detect and recognize an object within a scene, the features comprising the object must be identified and segregated from those comprising other objects. This problem is inherently difficult to solve because of the combinatorial nature of visual images. To appreciate this point, consider a simple local feature such as a small vertically oriented line segment placed within a fixed location of the visual field. When combined with other line segments, this feature can form a nearly infinite number of geometrical objects. Any one of these objects may coexist with an equally large number of other

Visual feature integration and the temporal correlation hypothesis

How are the functions performed by one part of the nervous system integrated with those of another? This fundamental issue pervades virtually every aspect of brain function from sensory and cognitive processing to motor control. Yet from a physiological perspective we know very little about the neural mechanisms underlying the integration of distributed processes in the nervous system. Even the simplest of sensori-motor acts engages vast numbers of cells in many different parts of the brain. Such actions require coordination between a host of neural systems, each of which must carry out parallel computations involving large populations of interconnected neurons. It seems reasonable to assume that a mechanism or class of mechanisms has evolved to temporally coordinate the activity within and between subsystems of the central nervous system. For several reasons, neuronal rhythms have long been thought to play an important role in such coordination. Since the discovery of the Electroencephalogram (EEG) over 60 years ago it has been known that a number of structures in the mammalian brain engage in rhythmic activities. These patterned neuronal oscillations take many forms. They occur over a broad range of frequencies, and are present in a multitude of different systems in the b~ain, during a variety of different behavioral states. They are often the most salient aspect of observable electrical activity in the brain and typically encompass widespread regions of cerebral tissue. With the advent of new techniques of multielectrode recording and neural imaging, it is now within the realm of possibility 'to record from 100 single neurons simultaneously (Wilson and McNaughton, 1993), to optically measure the activity in a cortical area (Blasdel and Salama, 1986; T'so et al., 1990), or to noninvasively image the pattern of electric current flow in an alert human being performing a task (Pantev et al., 1991). These new techniques have revealed that spatially and temporally organized activity among distributed populations of cells often takes the form of synchronous rhythms. When combined with cellular neurophysiological and anatomical studies these findings provide new insights into the behavior and mechanisms controUing the coordination of activity in neuronal populations.

Synchronous oscillations in neuronal systems: mechanisms and functions.

The ability to render objects invisible using a cloak (such that they are not detectable by an external observer) has long been a tantalizing goal1,2,3,4,5,6. Here, we demonstrate a cloak operating in the near infrared at a wavelength of 1,550 nm. The cloak conceals a deformation on a flat reflecting surface, under which an object can be hidden. The device has an area of 225 µm2 and hides a region of 1.6 µm2. It is composed of nanometre-size silicon structures with spatially varying densities across the cloak. The density variation is defined using transformation optics to define the effective index distribution of the cloak. A triangular array of silicon nanostructures is experimentally demonstrated to function as an optical cloaking device, operating in the near-infrared at a wavelength of 1550 nm. This approach could, in principle, be extended to larger areas using fabrication techniques such as nanoimprinting.

Silicon nanostructure cloak operating at optical frequencies

The outstanding feature of materials research in the eighties has been the convergence of basic research and practical application, leading to ever shorter cycles of innovation. This is especially true of materials which form the basis of key technologies. The mass storage units of the next generations of computers will be based on optical processes having a storage density which exceeds that of all hitherto known storage techniques that are practicable from the technical standpoint. In view of the fact that since 1982 read-only memories in the form of compact discs (CD-ROM) have become very successful in the field of audio electronics, research and development are now concentrated on materials for write-once (WORM) and erasable (EDRAW) storage systems. Suitable materials for optical data storage are substances in which data markings can be recorded and deleted respectively using semiconductor lasers. Materials development is centered on the synthesis of infrared-absorbing dyes for WORM memories and the production of rare earth/transition metal alloys for magneto-optical data recording. An introduction to CD-ROM technology will be followed by an overview of the state of development of the most important storage materials which are currently available commercially, and then the properties of these materials will be discussed with reference to selected examples.

Materials for Optical Data Storage

Suppose that a neuron is firing spontaneously or that it is firing under the influence of other neurons. Suppose that the data available are the firing times of the neurons present. An "integrate several inputs and fire" model is developed and studied empirically. For the model a neuron's firing occurs when an internal state variable crosses a random threshold. This conceptual model leads to maximum likelihood estimates of internal quantities, such as the postsynaptic potentials of the measured influencing neurons, the membrane potential, the absolute threshold and also estimates of derived quantities such as the strength-duration curve and the recovery process of the threshold. The model's validity is examined via an estimate of the conditional firing probability. The approach appears useful for estimating biologically meaningful parameters, for examining hypotheses re these parameters, for understanding the connections present in neural networks and for aiding description and classification of neurons and synapses. Analyses are presented for a number of data sets collected for the sea hare, Aplysia californica, by J. P. Segundo. Both excitatory and inhibitory examples are provided. The computations were carried out via the Glim statistical package. An example of a Glim program realizing the work is presented in the Appendix.

Maximum Likelihood Analysis of Spike Trains of Interacting Nerve Cells

The transient response of a lossless transmission line of finite length excited externally by an electromagnetic wave of an arbitrary waveform is studied. By using the Laplace transformation, a solution of the transient current is obtained as a series of analytical functions. Inspection of the solution leads to the conclusion that the response currents at terminals of the line can be equivalently expressed by the components induced at both terminals, and, in addition, the multireflected wave when the line is mismatched. Furthermore, a new equivalent circuit for the coupling phenomenon is discussed. >

Transient Response of a Transmission Line Excited by an Electromagnetic Pulse

Transient response of a transmission line excited by an electromagnetic pulse

The coupling between transmission lines crossing diagonally is described. The resulting equations are derived by neglecting the recoupling of the line under induction (acceptor line (AL)) to the exciter line (EL). The coupling coefficients of various models are also considered. Equivalent-circuit representations for finite-length lines are obtained in two different forms: one expressed in terms of ideal voltage and ideal current sources, and the other in terms of ideal transformers, mutual inductance, and mutual capacitance. The first expressions are useful for predicting coupling between various types of lines. The second shows the coupling mechanism. The validity of the theory is confirmed by the experimental studies.

Coupling Model of Crossing Transmission Lines

The spontaneous discharges which recorded extracellularly from cells in the lateral geniculate nucleus (LGN) of a cat were classified into the following 3 main groups depending upon the shapes of their interval histograms and autocorrelation functions: the gamma type whose interval histogram is fitted by a gamma distribution function and whose autocorrelation function has some periodic property which damps down within about several 10 ms, the burst type whose interval histogram has a peak in the first bin (less than 8 ms) and whose autocorrelation function has a large positive peak within several msec, and the multimodal type whose interval histogram has a complex shape with three or more peaks and whose autocorrelation function has a periodic property. Each type of spontaneous discharge seems to be inherent at scotopic and mesopic backgrounds, and the cells whose spontaneous discharges are the gamma type, the burst type, and the multimodal type are called here a gamma cell, burst cell, and the multimodal cell, respectively. Gamma cells are subdivided into X- and Y-cells (gamma-X and gamma-Y cells), but burst cells are all Y-cells and multimodal cells observed up to now are all X-cells. It is clear that these various types of cells are distributed significantly differently in each lamina. All the cells that we found up to now in lamina A were either burst cells or multimodal cells, but every type of cell was found in lamina A1. The majority of cells in lamina C were the gamma type. In most cases, the peak values of the PST histograms of gamma-Y cells (especially, on-center cells) are larger than those of burst cells. These results suggest that Y-cells projecting to area 17 from laminae A and A1 are the burst type, and Y-cells projecting to area 18 from laminae C and A1 are the gamma-Y type.

Statistical features of impulse trains in cat's lateral geniculate neurons

In this paper, the electrical constitutive parameters of various amorphous mixed materials are derived. The effective permittivity of the mixed materials can be expressed simply by the geometric mean of the permittivity of the series combination and that of the parallel combination of each material according to a dielectric layered model. In this expression, it is not necessary to specify the host material as was previously the case. The solution of various mixed materials can be directly expressed in the closed form. It is shown that there is a significant difference between the effective permittivity in this paper and that in the conventional theory depending on permittivity and loss of mixing materials. The validity of the approach is confirmed experimentally. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 88(10): 1–9, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20229

Risaburo Sato

Papers

Transient Response of a Transmission Line Excited by an Electromagnetic Pulse

Transient response of a transmission line excited by an electromagnetic pulse

Coupling Model of Crossing Transmission Lines

Statistical features of impulse trains in cat's lateral geniculate neurons

Effective permittivity of amorphous mixed materials