Post-traumatic epilepsyPost-traumatic epilepsy (PTE) is a form of acquired epilepsy that results from brain damage caused by physical trauma to the brain (traumatic brain injury, abbreviated TBI). A person with PTE experiences repeated post-traumatic seizures (PTS, seizures that result from TBI) more than a week after the initial injury. PTE is estimated to constitute 5% of all cases of epilepsy and over 20% of cases of acquired epilepsy (in which seizures are caused by an identifiable organic brain condition).
Generalized epilepsyGeneralized epilepsy is a form of epilepsy characterised by generalised seizures with no apparent cause. Generalized seizures, as opposed to focal seizures, are a type of seizure that impairs consciousness and distorts the electrical activity of the whole or a larger portion of the brain (which can be seen, for example, on electroencephalography, EEG). Generalized epilepsy is primary because the epilepsy is the originally diagnosed condition itself, as opposed to secondary epilepsy, which occurs as a symptom of a diagnosed condition.
Spike-and-waveSpike-and-wave is a pattern of the electroencephalogram (EEG) typically observed during epileptic seizures. A spike-and-wave discharge is a regular, symmetrical, generalized EEG pattern seen particularly during absence epilepsy, also known as ‘petit mal’ epilepsy. The basic mechanisms underlying these patterns are complex and involve part of the cerebral cortex, the thalamocortical network, and intrinsic neuronal mechanisms. The first spike-and-wave pattern was recorded in the early twentieth century by Hans Berger.
NeurologyNeurology (from νεῦρον (neûron), "string, nerve" and the suffix -logia, "study of") is the branch of medicine dealing with the diagnosis and treatment of all categories of conditions and disease involving the nervous system, which comprises the brain, the spinal cord and the peripheral nerves. Neurological practice relies heavily on the field of neuroscience, the scientific study of the nervous system. A neurologist is a physician specializing in neurology and trained to investigate, diagnose and treat neurological disorders.
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
LearningLearning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.
