There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
Body size and metabolic rate both fundamentally constrain how species interact with their environment, and hence ultimately affect their niche. While many mechanisms leading to these constraints have been explored, their effects on the resolution at which temporal information is perceived have been largely overlooked. The visual system acts as a gateway to the dynamic environment and the relative resolution at which organisms are able to acquire and process visual information is likely to restrict their ability to interact with events around them. As both smaller size and higher metabolic rates should facilitate rapid behavioural responses, we hypothesized that these traits would favour perception of temporal change over finer timescales. Using critical flicker fusion frequency, the lowest frequency of flashing at which a flickering light source is perceived as constant, as a measure of the maximum rate of temporal information processing in the visual system, we carried out a phylogenetic comparative analysis of a wide range of vertebrates that supported this hypothesis. Our results have implications for the evolution of signalling systems and predator-prey interactions, and, combined with the strong influence that both body mass and metabolism have on a species' ecological niche, suggest that time perception may constitute an important and overlooked dimension of niche differentiation.
Studies examining the relation of information processing speed, as measured by reaction time, with mortality are scarce. We explored these associations in a representative sample of the US population.
Re-entrant feedback, either within sensory cortex or arising from prefrontal areas, has been strongly linked to the emergence of consciousness, both in theoretical and experimental work. This idea, together with evidence for local micro-consciousness, suggests the generation of qualia could in some way result from local network activity under re-entrant activation. This paper explores the possibility by examining the processing of information by local cortical networks. It highlights the difference between the information structure (how the information is physically embodied), and the information message (what the information is about). It focuses on the network’s ability to recognize information structures amongst its inputs under conditions of extensive local feedback, and to then assign information messages to those structures. It is shown that if the re-entrant feedback enables the network to achieve an attractor state, then the message assigned in any given pass of information through the network is a representation of the message assigned in the previous pass-through of information. Based on this ability the paper argues that as information is repeatedly cycled through the network, the information message that is assigned evolves from a recognition of what the input structure is, to what it is like, to how it appears, to how it seems. It could enable individual networks to be the site of qualia generation. The paper goes on to show networks in cortical layers 2/3 and 5a have the connectivity required for the behavior proposed, and reviews some evidence for a link between such local cortical cyclic activity and conscious percepts. It concludes with some predictions based on the theory discussed.
Frankel and colleagues have compared Israel and the U.S.’s experiences with health information exchange (HIE). They highlight the importance of institutional factors in fostering HIE development, notably the influence of local structures, experience and incentives. Historically, information infrastructure in the U.S. has been limited due to lack of standards, fragmented institutions and competition. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 authorized billions of dollars for the adoption and “Meaningful Use” of electronic health records. HITECH programs and Meaningful Use incentives target the advancement of HIE through 1) building blocks, 2) local support and 3) payment incentives. Meaningful Use requirements create a roadmap to broader electronic exchange of health information among providers and with patients. Ultimately, successful HIE in the U.S. will depend on whether Meaningful Use can address institutional needs within local markets.This is a commentary on http://www.ijhpr.org/content/2/1/722.
An increasing number of publishers and funding agencies require public data archiving (PDA) in open-access databases. PDA has obvious group benefits for the scientific community, but many researchers are reluctant to share their data publicly because of real or perceived individual costs. Improving participation in PDA will require lowering costs and/or increasing benefits for primary data collectors. Small, simple changes can enhance existing measures to ensure that more scientific data are properly archived and made publicly available: (1) facilitate more flexible embargoes on archived data, (2) encourage communication between data generators and re-users, (3) disclose data re-use ethics, and (4) encourage increased recognition of publicly archived data.
Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.
Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.
A major challenge since the invention of implantable devices has been a reliable and long-term stable transcutaneous communication. In the case of prosthetic limbs, existing neuromuscular interfaces have been unable to address this challenge and provide direct and intuitive neural control. Although prosthetic hardware and decoding algorithms are readily available, there is still a lack of appropriate and stable physiological signals for controlling the devices. We developed a percutaneous osseointegrated (bone-anchored) interface that allows for permanent and unlimited bidirectional communication with the human body. With this interface, an artificial limb can be chronically driven by implanted electrodes in the peripheral nerves and muscles of an amputee, outside of controlled environments and during activities of daily living, thus reducing disability and improving quality of life. We demonstrate in one subject, for more than 1 year, that implanted electrodes provide a more precise and reliable control than surface electrodes, regardless of limb position and environmental conditions, and with less effort. Furthermore, long-term stable myoelectric pattern recognition and appropriate sensory feedback elicited via neurostimulation was demonstrated. The opportunity to chronically record and stimulate the neuromuscular system allows for the implementation of intuitive control and naturally perceived sensory feedback, as well as opportunities for the prediction of complex limb motions and better understanding of sensory perception. The permanent bidirectional interface presented here is a critical step toward more natural limb replacement, by combining stable attachment with permanent and reliable human-machine communication.
Online traces of human activity offer novel opportunities to study the dynamics of complex knowledge exchange networks, in particular how emergent patterns of collective attention determine what new information is generated and consumed. Can we measure the relationship between demand and supply for new information about a topic? We propose a normalization method to compare attention bursts statistics across topics with heterogeneous distribution of attention. Through analysis of a massive dataset on traffic to Wikipedia, we find that the production of new knowledge is associated to significant shifts of collective attention, which we take as proxy for its demand. This is consistent with a scenario in which allocation of attention toward a topic stimulates the demand for information about it, and in turn the supply of further novel information. However, attention spikes only for a limited time span, during which new content has higher chances of receiving traffic, compared to content created later or earlier on. Our attempt to quantify demand and supply of information, and our finding about their temporal ordering, may lead to the development of the fundamental laws of the attention economy, and to a better understanding of social exchange of knowledge information networks.