Robustness of Planar Fourier Capture Arrays to Colour Changes and Lost Pixels

Planar Fourier capture arrays (PFCAs) are optical sensors built entirely in standard microchip manufacturing flows. PFCAs are composed of ensembles of angle sensitive pixels (ASPs) that each report a single coefficient of the Fourier transform of the far-away scene. Here we characterize the performance of PFCAs under the following three non-optimal conditions. First, we show that PFCAs can operate while sensing light of a wavelength other than the design point. Second, if only a randomly-selected subset of 10% of the ASPs are functional, we can nonetheless reconstruct the entire far-away scene using compressed sensing. Third, if the wavelength of the imaged light is unknown, it can be inferred by demanding self-consistency of the outputs.Comment: 15 pages including cover page, 12 figures, associated with the 9th International Conference on Position Sensitive Detector

Impacts on prenatal development of the human cerebellum: a systematic review

Purpose: The cerebellum is essential for normal neurodevelopment and is particularlysusceptible for intra-uterine disruptions. Although some causal prenatal exposures havebeen identified, the origin of neurodevelopmental disorders remains mostly unclear. Therefore,a systematic literature search was conducted to provide an overview of parental environmentalexposures and intrinsic factors influencing prenatal cerebellar growth and development inhumans. Materials and methods: The literature search was limited to human studies in the Englishlanguage and was conducted in Embase, Medline, Cochrane, Web of Science, Pubmed andGoogleScholar. Eligible studies were selected by three independent reviewers and study qualitywas scored by two independent reviewers. Results: The search yielded 3872 articles. We found 15 eligible studies reporting associationsbetween cerebellar development and maternal smoking (4), use of alcohol (3),in vitrofertilization mediums (1), mercury (1), mifepristone (2), aminopropionitriles (1), ethnicity (2) andcortisol levels (1). No studies reported on paternal factors. Conclusions: Current literature on associations between parental environmental exposures,intrinsic factors and human cerebellar development is scarce. Yet, this systematic reviewprovided an essential overview of human studies demonstrating the vulnerability of thecerebellum to the intra-uterine environment

Encoding and decoding models in cognitive electrophysiology

Contains fulltext : 179395.pdf (publisher's version ) (Open Access)Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.24 p

Sparse ensemble neural code for a complete vocal repertoire

Summary: The categorization of animal vocalizations into distinct behaviorally relevant groups for communication is an essential operation that must be performed by the auditory system. This auditory object recognition is a difficult task that requires selectivity to the group identifying acoustic features and invariance to renditions within each group. We find that small ensembles of auditory neurons in the forebrain of a social songbird can code the bird’s entire vocal repertoire (∼10 call types). Ensemble neural discrimination is not, however, correlated with single unit selectivity, but instead with how well the joint single unit tunings to characteristic spectro-temporal modulations span the acoustic subspace optimized for the discrimination of call types. Thus, akin to face recognition in the visual system, call type recognition in the auditory system is based on a sparse code representing a small number of high-level features and not on highly selective grandmother neurons