Encoding mechanisms based on fast oscillations in the retina of the cat and their dependencies on anesthesia

Processing in the visual system starts in the retina. Its complex network of cells with different properties enables for parallel encoding and transmission of visual information to the lateral geniculate nucleus (LGN) and to the cortex. In the retina, it has been shown that responses are often accompanied by fast synchronous oscillations (30 - 90 Hz) in a stimulus-dependent manner. Studies in the frog, rabbit, cat and monkey, have shown strong oscillatory responses to large stimuli which probably encode global stimulus properties, such as size and continuity (Neuenschwander and Singer, 1996; Ishikane et al., 2005). Moreover, simultaneous recordings from different levels in the visual system have demonstrated that the oscillatory patterning of retinal ganglion cell responses are transmitted to the cortex via the LGN (Castelo-Branco et al., 1998). Overall these results suggest that feedforward synchronous oscillations contribute to visual encoding. In the present study on the LGN of the anesthetized cat, we further investigate the role of retinal oscillations in visual processing by applying complex stimuli, such as natural visual scenes, light spots of varying size and contrast, and flickering checkerboards. This is a necessary step for understanding encoding mechanisms in more naturalistic conditions, as currently most data on retinal oscillations have been limited to simple, flashed and stationary stimuli. Correlation analysis of spiking responses confirmed previous results showing that oscillatory responses in the retina (observed here from the LGN responses) largely depend on the size and stationarity of the stimulus. For natural scenes (gray-level and binary movies) oscillations appeared only for brief moments probably when receptive fields were dominated by large continuous, flat-contrast surfaces. Moreover, oscillatory responses to a circle stimulus could be broken with an annular mask indicating that synchronization arises from relatively local interactions among populations of activated cells in the retina. A surprising finding in this study was that retinal oscillations are highly dependent on halothane anesthesia levels. In the absence of halothane, oscillatory activity vanished independent of the characteristics of the stimuli. The same results were obtained for isoflurane, which has similar pharmacological properties. These new and unexpected findings question whether feedfoward oscillations in the early visual system are simply due to an imbalance between excitation and inhibition in the retinal networks generated by the halogenated anesthetics. Further studies in awake behaving animals are necessary to extend these conclusions

Uma fundamentação matemática para processamento digital de sinais intervalares

This work deals with a mathematical fundament for digital signal processing under point view of interval mathematics. Intend treat the open problem of precision and repesention of data in digital systems, with a intertval version of signals representation. Signals processing is a rich and complex area, therefore, this work makes a cutting with focus in systems linear invariant in the time. A vast literature in the area exists, but, some concepts in interval mathematics need to be redefined or to be elaborated for the construction of a solid theory of interval signal processing. We will construct a basic fundaments for signal processing in the interval version, such as basic properties linearity, stability, causality, a version to intervalar of linear systems e its properties. They will be presented interval versions of the convolution and the Z-transform. Will be made analysis of convergences of systems using interval Z-transform , a essentially interval distance, interval complex numbers , application in a interval filter.

Uso de correntropia na generalização de funções cicloestacionárias e aplicações para a extração de características de sinais modulados

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.

Slow Oscillations in the Mouse Hippocampus Entrained by Nasal Respiration

Different types of network oscillations occur in different behavioral, cognitive, or vigilance states. The rodent hippocampus expresses prominentoscillations atfrequencies between 4 and 12Hz,which are superimposed by phase-coupledoscillations (30 –100Hz).These patterns entrain multineuronal activity over large distances and have been implicated in sensory information processing and memory formation. Here we report a new type of oscillation at near- frequencies (2– 4 Hz) in the hippocampus of urethane-anesthetized mice. The rhythm is highly coherent with nasal respiration and with rhythmic field potentials in the olfactory bulb: hence, we called it hippocampal respiration-induced oscillations. Despite the similarity in frequency range, several features distinguish this pattern from locally generatedoscillations: hippocampal respiration-induced oscillations have a unique laminar amplitude profile, are resistant to atropine, couple differentlytooscillations, and are abolished when nasal airflow is bypassed bytracheotomy. Hippocampal neurons are entrained by both the respiration-induced rhythm and concurrent oscillations, suggesting a direct interaction between endogenous activity in the hippocampus and nasal respiratory inputs. Our results demonstrate that nasal respiration strongly modulates hippocampal network activity in mice, providing a long-range synchronizing signal between olfactory and hippocampal networks.