A 65-nm CMOS Lossless Bio-Signal Compression Circuit With 250 FemtoJoule Performance Per Bit.

A 65 nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel 'xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the 'simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios.

Miniature Untethered EEG Recorder Improves Advanced Neuroscience Methodologies.

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer's disease (AD) mouse model.

Hadoop neural network for parallel and distributed feature selection.

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses.

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience.

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications.

On the identification of sleep stages in mouse electroencephalography time-series.

The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory.

The CARMEN software as a service infrastructure.

The CARMEN platform allows neuroscientists to share data, metadata, services and workflows, and to execute these services and workflows remotely via a Web portal. This paper describes how we implemented a service-based infrastructure into the CARMEN Virtual Laboratory. A Software as a Service framework was developed to allow generic new and legacy code to be deployed as services on a heterogeneous execution framework.

CARMEN: a practical approach to metadata management.

The Code Analysis Repository & Modelling for E-Neuroscience (CARMEN) project aims to enable broad sharing of resources, through the provision of a secure, online environment for storage and curation of data, analysis code and experimental protocols, together with the ability to execute data analysis. While the CARMEN system is initially focused on electrophysiology data, it is equally applicable to many domains outside neuroscience. Metadata are essential for a system such as CARMEN that has the potential to store thousands of data collections and analysis codes; without metadata, resource discovery, interpretation, evaluation and re-use would be severely impeded.

Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services.

The class of 2005. United States: two scientists and a baby.

If you trust the conventional wisdom, Amy Palmer and Alexis Templeton did a lot of things wrong in their job search. Then why did things turn out so right?