Random synaptic feedback weights support error backpropagation for deep learning

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning

613 cases of splenic rupture without risk factors or previously diagnosed disease: a systematic review

Background Rupture of the spleen in the absence of trauma or previously diagnosed disease is largely ignored in the emergency literature and is often not documented as such in journals from other fields. We have conducted a systematic review of the literature to highlight the surprisingly frequent occurrence of this phenomenon and to document the diversity of diseases that can present in this fashion. Methods Systematic review of English and French language publications catalogued in Pubmed, Embase and CINAHL between 1950 and 2011. Results We found 613 cases of splenic rupture meeting the criteria above, 327 of which occurred as the presenting complaint of an underlying disease and 112 of which occurred following a medical procedure. Rupture appeared to occur spontaneously in histologically normal (but not necessarily normal size) spleens in 35 cases and after minor trauma in 23 cases. Medications were implicated in 47 cases, a splenic or adjacent anatomical abnormality in 31 cases and pregnancy or its complications in 38 cases. The most common associated diseases were infectious (n = 143), haematologic (n = 84) and non-haematologic neoplasms (n = 48). Amyloidosis (n = 24), internal trauma such as cough or vomiting (n = 17) and rheumatologic diseases (n = 10) are less frequently reported. Colonoscopy (n = 87) was the procedure reported most frequently as a cause of rupture. The anatomic abnormalities associated with rupture include splenic cysts (n = 6), infarction (n = 6) and hamartomata (n = 5). Medications associated with rupture include anticoagulants (n = 21), thrombolytics (n = 13) and recombinant G-CSF (n = 10). Other causes or associations reported very infrequently include other endoscopy, pulmonary, cardiac or abdominal surgery, hysterectomy, peliosis, empyema, remote pancreato-renal transplant, thrombosed splenic vein, hemangiomata, pancreatic pseudocysts, splenic artery aneurysm, cholesterol embolism, splenic granuloma, congenital diaphragmatic hernia, rib exostosis, pancreatitis, Gaucher's disease, Wilson's disease, pheochromocytoma, afibrinogenemia and ruptured ectopic pregnancy. Conclusions Emergency physicians should be attuned to the fact that rupture of the spleen can occur in the absence of major trauma or previously diagnosed splenic disease. The occurrence of such a rupture is likely to be the manifesting complaint of an underlying disease. Furthermore, colonoscopy should be more widely documented as a cause of splenic rupture

Inertial Sensors and Their Applications

Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and industrial applications for accelerometers and gyroscopes. The multiformity of applications creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. This makes it challenging to choose sensors that are suited best for the particular application. In addition, development of signal processing algorithms for inertial sensor data require understanding on the physical principles of both motion generated and sensor operation principles. This chapter aims to aid the system designer to understand and manage these challenges. The principles of operation of accelerometers and gyroscopes are explained with examples of different applications using inertial sensors data as input. Especially, detailed examples of signal processing algorithms for pedestrian navigation and motion classification are given.acceptedVersionPeer reviewe