Spiking neural networks for computer vision

State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities

Lost in reviews:Looking for the involvement of stakeholders, patients, public and other non-researcher contributors in realist reviews

The involvement of non‐researcher contributors (eg, stakeholders, patients and the public, decision and policy makers, experts, lay contributors) has taken a variety of forms within evidence syntheses. Realist reviews are a form of evidence synthesis that involves non‐researcher contributors yet this practice has received little attention. In particular, the role of patient and public involvement (PPI) has not been clearly documented. This review of reviews describes the ways in which contributor involvement, including PPI, is documented within healthcare realist reviews published over the last five years. A total of 448 papers published between 2014 and 2019 were screened, yielding 71 full‐text papers included in this review. Statements about contributor involvement were synthesized across each review using framework analysis. Three themes are described in this article including nomenclature, nature of involvement, and reporting impact.Papers indicate that contributor involvement in realist reviews refers to stakeholders, experts, or advisory groups (ie, professionals, clinicians, or academics). Patients and the public are occasionally subsumed into these groups and in doing so, the nature and impact of their involvement become challenging to identify and at times, is lost completely. Our review findings indicate a need for the realist review community to develop guidance to support researchers in their future collaboration with contributors, including patients and the public