Traumatic brain injuryA traumatic brain injury (TBI), also known as an intracranial injury, is an injury to the brain caused by an external force. TBI can be classified based on severity ranging from mild traumatic brain injury (mTBI/concussion) to severe traumatic brain injury. TBI can also be characterized based on mechanism (closed or penetrating head injury) or other features (e.g., occurring in a specific location or over a widespread area). Head injury is a broader category that may involve damage to other structures such as the scalp and skull.
Spiking neural networkArtificial neural network Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold.
Generative modelIn statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, following : A generative model is a statistical model of the joint probability distribution on given observable variable X and target variable Y; A discriminative model is a model of the conditional probability of the target Y, given an observation x; and Classifiers computed without using a probability model are also referred to loosely as "discriminative".
Foundation modelsA foundation model (also called base model) is a large machine learning (ML) model trained on a vast quantity of data at scale (often by self-supervised learning or semi-supervised learning) such that it can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how artificial intelligence (AI) systems are built, such as by powering prominent chatbots and other user-facing AI.
ElectrocorticographyElectrocorticography (ECoG), a type of intracranial electroencephalography (iEEG), is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. In contrast, conventional electroencephalography (EEG) electrodes monitor this activity from outside the skull. ECoG may be performed either in the operating room during surgery (intraoperative ECoG) or outside of surgery (extraoperative ECoG).
Conditional random fieldConditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. What kind of graph is used depends on the application.
Brain deathBrain death is the permanent, irreversible, and complete loss of brain function which may include cessation of involuntary activity necessary to sustain life. It differs from persistent vegetative state, in which the person is alive and some autonomic functions remain. It is also distinct from comas as long as some brain and bodily activity and function remain, and it is also not the same as the condition locked-in syndrome. A differential diagnosis can medically distinguish these differing conditions.
Signs and symptomsSigns and symptoms are the observed or detectable signs, and experienced symptoms of an illness, injury, or condition. Signs are objective and externally observable; symptoms are a person's reported subjective experiences. A sign for example may be a higher or lower temperature than normal, raised or lowered blood pressure or an abnormality showing on a medical scan. A symptom is something out of the ordinary that is experienced by an individual such as feeling feverish, a headache or other pains in the body.
Epilepsy surgeryEpilepsy surgery involves a neurosurgical procedure where an area of the brain involved in seizures is either resected, ablated, disconnected or stimulated. The goal is to eliminate seizures or significantly reduce seizure burden. Approximately 60% of all people with epilepsy (0.4% of the population of industrialized countries) have focal epilepsy syndromes. In 15% to 20% of these patients, the condition is not adequately controlled with anticonvulsive drugs. Such patients are potential candidates for surgical epilepsy treatment.
Learning to rankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item.
Image segmentationIn and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
Machine translationMachine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.
