Accelerated Compressed Sensing Based CT Image Reconstruction.

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

Odd-symmetry phase gratings produce optical nulls uniquely insensitive to wavelength and depth.

This Letter presents the analysis of a new class of diffractive optical element, the odd-symmetry phase grating, which creates wavelength- and depth-robust features in its near-field diffraction pattern.

Hippocampal episode fields develop with learning.

Several recent studies have shown that hippocampal neurons fire during the delay period in between trials and that these firing patterns differ when different behaviors are required, suggesting that the neuronal responses may be involved in maintaining the memories needed for the upcoming trial. In particular, one study found that hippocampal neurons reliably fired at particular times, referred to as "episode fields" (EFs), during the delay period of a spatial alternation task (Pastalkova et al. (2008) Science 321:1322-1327). The firing of these neurons resulted in distinct sequential firing patterns on left and right turn trials, and these firing patterns could be used to predict the upcoming behavioral response. In this study, we examined neuronal firing during the delay period of a hippocampus-dependent plus maze task, which involved learning to approach two different reward locations (east and west), and we examined the development of these firing patterns with learning. As in the previous study, hippocampal neurons exhibited discrete periods of elevated firing during the delay (EFs) and the firing patterns were distinct on the east and west trials. Moreover, these firing patterns emerged and began to differentiate the east and west conditions during the first training session and continued to develop as the rats learned the task. The finding of similar firing patterns in different tasks suggests that the EFs are a robust phenomenon, which may occur whenever subjects must maintain distinct memory representations during a delay period. Additionally, in the previous study (Pastalkova et al. (2008) Science 321:1322-1327), the distinct firing patterns could have been due to the differing goal locations, behavioral responses (left or right turns), or trajectories. In this study, neuronal firing varied with the goal location regardless of the trajectories or responses, suggesting that the firing patterns encode the behavioral context rather than specific behaviors.

Anthropic Correction of Information Estimates and Its Application to Neural Coding.

Information theory has been used as an organizing principle in neuroscience for several decades. Estimates of the mutual information (MI) between signals acquired in neurophysiological experiments are believed to yield insights into the structure of the underlying information processing architectures. With the pervasive availability of recordings from many neurons, several information and redundancy measures have been proposed in the recent literature. A typical scenario is that only a small number of stimuli can be tested, while ample response data may be available for each of the tested stimuli. The resulting asymmetric information estimation problem is considered. It is shown that the direct plug-in information estimate has a negative bias. An anthropic correction is introduced that has a positive bias. These two complementary estimators and their combinations are natural candidates for information estimation in neuroscience. Tail and variance bounds are given for both estimates. The proposed information estimates are applied to the analysis of neural discrimination and redundancy in the avian auditory system.

Functional groups in the avian auditory system.

Auditory perception depends on the coding and organization of the information-bearing acoustic features of sounds by auditory neurons. We report here that auditory neurons can be classified into functional groups, each of which plays a specific role in extracting distinct complex sound features. We recorded the electrophysiological responses of single auditory neurons in the songbird midbrain and forebrain to conspecific song, measured their tuning by calculating spectrotemporal receptive fields (STRFs), and classified them using multiple cluster analysis methods. Based on STRF shape, cells clustered into functional groups that divided the space of acoustical features into regions that represent cues for the fundamental acoustic percepts of pitch, timbre, and rhythm. Four major groups were found in the midbrain, and five major groups were found in the forebrain. Comparing STRFs in midbrain and forebrain neurons suggested that both inheritance and emergence of tuning properties occur as information ascends the auditory processing stream.

Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain.

Physiological studies in vocal animals such as songbirds indicate that vocalizations drive auditory neurons particularly well. But the neural mechanisms whereby vocalizations are encoded differently from other sounds in the auditory system are unknown. We used spectrotemporal receptive fields (STRFs) to study the neural encoding of song versus the encoding of a generic sound, modulation-limited noise, by single neurons and the neuronal population in the zebra finch auditory midbrain. The noise was designed to match song in frequency, spectrotemporal modulation boundaries, and power. STRF calculations were balanced between the two stimulus types by forcing a common stimulus subspace. We found that 91% of midbrain neurons showed significant differences in spectral and temporal tuning properties when birds heard song and when birds heard modulation-limited noise. During the processing of noise, spectrotemporal tuning was highly variable across cells. During song processing, the tuning of individual cells became more similar; frequency tuning bandwidth increased, best temporal modulation frequency increased, and spike timing became more precise. The outcome was a population response to song that encoded rapidly changing sounds with power and precision, resulting in a faithful neural representation of the temporal pattern of a song. Modeling responses to song using the tuning to modulation-limited noise showed that the population response would not encode song as precisely or robustly. We conclude that stimulus-dependent changes in auditory tuning during song processing facilitate the high-fidelity encoding of the temporal pattern of a song.