Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.

Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and are associated with impaired memory and cognition. Despite growing interest, most studies involving IED detections rely on visual annotations or employ simple amplitude threshold approaches.

Quantification of Osteoclasts in Culture, Powered by Machine Learning.

osteoclastogenesis is a central assay in bone biology to study the effect of genetic and pharmacologic cues on the differentiation of bone resorbing osteoclasts. To date, identification of TRAP+ multinucleated cells and measurements of osteoclast number and surface rely on a manual tracing requiring specially trained lab personnel. This task is tedious, time-consuming, and prone to operator bias.

Rich cell-type-specific network topology in neocortical microcircuitry.

Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a ∼0.3-mm volume from juvenile rat somatosensory neocortex, comprising ∼31,000 cells and ∼36 million synapses.

Biogerontology research in Israel.

Studies on biogerontology in Israel are reviewed in relation to the academic and medical research setup, as well as to a variety of gerontological bodies that contribute to promotion of the research. Studies on the biology of aging are outlined with a view also on the relevance and possible applications to medicine. The various topics encompass longevity-associated genes, effects of calorie restriction, including studies on the experimental model of the alpha-MUPA mutant mouse, as well as basic issues regarding the central nervous system and skeletal tissues.

Past-future information bottleneck in dynamical systems.

Biological systems need to process information in real time and must trade off accuracy of presentation and coding costs. Here we operationalize this trade-off and develop an information-theoretic framework that selectively extracts information of the input past that is predictive about the output future, obtaining a generalized eigenvalue problem. Thereby, we unravel the input history in terms of structural phase transitions corresponding to additional dimensions of a state space.