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Predictive Coding
In neuroscience, predictive coding (also known as predictive processing) is a theory of brain function which postulates that the brain is constantly generating and updating a " mental model" of the environment. According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses. Predictive coding is member of a wider set of theories that follow the Bayesian brain hypothesis. Origins Theoretical ancestors to predictive coding date back as early as 1860 with Helmholtz's concept of unconscious inference. Unconscious inference refers to the idea that the human brain fills in visual information to make sense of a scene. For example, if something is relatively smaller than another object in the visual field, the brain uses that information as a likely cue of depth, such that the perceiver ultimately (and involuntarily) experiences depth. The understanding of perception as the interaction ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuroscience
Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions, and its disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. The techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain. Hist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unsupervised Learning
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision. Some researchers consider self-supervised learning a form of unsupervised learning. Conceptually, unsupervised learning divides into the aspects of data, training, algorithm, and downstream applications. Typically, the dataset is harvested cheaply "in the wild", such as massive text corpus obtained by web crawling, with only minor filtering (such as Common Crawl). This compares favorably to supervised learning, where the dataset (such as the ImageNet1000) is typically constructed manually, which is much more expensive. There were algorithms designed specifically for unsupervised learning, such as clustering algorithms like k-means, dimensionality reduction techniques l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Active Inference
Active may refer to: Music * ''Active'' (album), a 1992 album by Casiopea * "Active" (song), a 2024 song by Asake and Travis Scott from Asake's album ''Lungu Boy'' * Active Records, a record label Ships * ''Active'' (ship), several commercial ships by that name * HMS ''Active'', the name of various ships of the British Royal Navy * USCS ''Active'', a US Coast Survey ship in commission from 1852 to 1861 * USCGC ''Active'', the name of various ships of the US Coast Guard * USRC ''Active'', the name of various ships of the US Revenue Cutter Service * USS ''Active'', the name of various ships of the US Navy Computers and electronics * Active Enterprises, a defunct video game developer * Sky Active, the brand name for interactive features on Sky Digital available in the UK and Ireland * Active (software), software used for open publishing by Indymedia; see Independent Media Center * The "live" circuit of mains power in countries observing AS/NZS 3112 electrical stan ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reticular Activating System
The reticular formation is a set of interconnected nuclei in the brainstem that spans from the lower end of the medulla oblongata to the upper end of the midbrain. The neurons of the reticular formation make up a complex set of neural networks in the core of the brainstem. The reticular formation is made up of a diffuse net-like formation of reticular nuclei which is not well-defined. It may be seen as being made up of all the interspersed cells in the brainstem between the more compact and named structures. The reticular formation is functionally divided into the ascending reticular activating system (ARAS), ascending pathways to the cerebral cortex, and the descending reticular system, descending pathways ( reticulospinal tracts) to the spinal cord. Due to its extent along the brainstem it may be divided into different areas such as the midbrain reticular formation, the central mesencephalic reticular formation, the pontine reticular formation, the paramedian pontine reticul ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attention
Attention or focus, is the concentration of awareness on some phenomenon to the exclusion of other stimuli. It is the selective concentration on discrete information, either subjectively or objectively. William James (1890) wrote that "Attention is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration, of consciousness are of its essence." Attention has also been described as the allocation of limited cognitive processing resources. Attention is manifested by an attentional bottleneck, in terms of the amount of data the brain can process each second; for example, in human vision, less than 1% of the visual input data stream of 1MByte/sec can enter the bottleneck, leading to inattentional blindness. Attention remains a crucial area of investigation within education, psychology, neuroscience, cognitive neuroscience, and neuropsychology. Areas of activ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kalman Filter
In statistics and control theory, Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán. Kalman filtering has numerous technological applications. A common application is for guidance, navigation, and control of vehicles, particularly aircraft, spacecraft and ships Dynamic positioning, positioned dynamically. Furthermore, Kalman filtering is much applied in time series analysis tasks such as signal processing and econometrics. K ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Filtering
In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for estimating an unknown probability density function (PDF) recursively over time using incoming measurements and a mathematical process model. The process relies heavily upon mathematical concepts and models that are theorized within a study of prior and posterior probabilities known as Bayesian statistics. In robotics A Bayes filter is an algorithm used in computer science for calculating the probabilities of multiple beliefs to allow a robot to infer its position and orientation. Essentially, Bayes filters allow robots to continuously update their most likely position within a coordinate system, based on the most recently acquired sensor data. This is a recursive algorithm. It consists of two parts: prediction and innovation. If the variables are normally distributed and the transitions are linear, the Bayes filter becomes eq ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Noise (signal Processing)
In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion.Vyacheslav Tuzlukov (2010), ''Signal Processing Noise'', Electrical Engineering and Applied Signal Processing Series, CRC Press. 688 pages. Sometimes the word is also used to mean signals that are random (Predictability, unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filter (signal processing), filters. The mathematical limits for noise removal are set by information theory. Types of noise Signal processing noise can be classified by its statistical properties (sometimes called the "colors of noise, color" of the noi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Belief Network
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification. DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Helmholtz Machines
The Helmholtz machine (named after Hermann von Helmholtz and his concept of Helmholtz free energy) is a type of artificial neural network that can account for the hidden structure of a set of data by being trained to create a generative model of the original set of data. The hope is that by learning economical representations of the data, the underlying structure of the generative model should reasonably approximate the hidden structure of the data set. A Helmholtz machine contains two networks, a bottom-up ''recognition'' network that takes the data as input and produces a distribution over hidden variables, and a top-down "generative" network that generates values of the hidden variables and the data itself. At the time, Helmholtz machines were one of a handful of learning architectures that used feedback as well as feedforward to ensure quality of learned models. Helmholtz machines are usually trained using an unsupervised learning algorithm, such as the wake-sleep algorithm. T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Receptive Field
The receptive field, or sensory space, is a delimited medium where some physiological stimuli can evoke a sensory neuronal response in specific organisms. Complexity of the receptive field ranges from the unidimensional chemical structure of odorants to the multidimensional spacetime of human visual field, through the bidimensional skin surface, being a receptive field for touch perception. Receptive fields can positively or negatively alter the membrane potential with or without affecting the rate of action potentials. A sensory space can be dependent of an animal's location. For a particular sound wave traveling in an appropriate transmission medium, by means of sound localization, an auditory space would amount to a reference system that continuously shifts as the animal moves (taking into consideration the space inside the ears as well). Conversely, receptive fields can be largely independent of the animal's location, as in the case of place cells. A sensory space can also ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sparse Coding
Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the Stimulus (physiology), stimulus and the neuronal responses, and the relationship among the Electrophysiology, electrical activities of the neurons in the Neuronal ensemble, ensemble. Based on the theory that sensory and other information is represented in the brain by Biological neural network, networks of neurons, it is believed that neurons can encode both Digital data, digital and analog signal, analog information. Overview Neurons have an ability uncommon among the cells of the body to propagate signals rapidly over large distances by generating characteristic electrical pulses called action potentials: voltage spikes that can travel down axons. Sensory neurons change their activities by firing sequences of action potentials in various temporal patterns, with the presence of external sensory stimuli, such as light, sound, taste, Olfaction, sm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
