Physical neural networkA physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.
Feedforward neural networkA feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Music sequencerA music sequencer (or audio sequencer or simply sequencer) is a device or application software that can record, edit, or play back music, by handling note and performance information in several forms, typically CV/Gate, MIDI, or Open Sound Control (OSC), and possibly audio and automation data for digital audio workstations (DAWs) and plug-ins. The advent of Musical Instrument Digital Interface (MIDI) and the Atari ST home computer in the 1980s gave programmers the opportunity to design software that could more easily record and play back sequences of notes played or programmed by a musician.
Musical improvisationMusical improvisation (also known as musical extemporization) is the creative activity of immediate ("in the moment") musical composition, which combines performance with communication of emotions and instrumental technique as well as spontaneous response to other musicians. Sometimes musical ideas in improvisation are spontaneous, but may be based on chord changes in classical music and many other kinds of music. One definition is a "performance given extempore without planning or preparation".
Neuroscience of musicThe neuroscience of music is the scientific study of brain-based mechanisms involved in the cognitive processes underlying music. These behaviours include music listening, performing, composing, reading, writing, and ancillary activities. It also is increasingly concerned with the brain basis for musical aesthetics and musical emotion. Scientists working in this field may have training in cognitive neuroscience, neurology, neuroanatomy, psychology, music theory, computer science, and other relevant fields.
Memory consolidationMemory consolidation is a category of processes that stabilize a memory trace after its initial acquisition. A memory trace is a change in the nervous system caused by memorizing something. Consolidation is distinguished into two specific processes. The first, synaptic consolidation, which is thought to correspond to late-phase long-term potentiation, occurs on a small scale in the synaptic connections and neural circuits within the first few hours after learning.
Input deviceIn computing, an input device is a piece of equipment used to provide data and control signals to an information processing system, such as a computer or information appliance. Examples of input devices include keyboards, mouse, s, cameras, joysticks, and microphones. Input devices can be categorized based on: modality of input (e.g., mechanical motion, audio, visual, etc.) whether the input is discrete (e.g., pressing of key) or continuous (e.g.
Visual memoryVisual memory describes the relationship between perceptual processing and the encoding, storage and retrieval of the resulting neural representations. Visual memory occurs over a broad time range spanning from eye movements to years in order to visually navigate to a previously visited location. Visual memory is a form of memory which preserves some characteristics of our senses pertaining to visual experience. We are able to place in memory visual information which resembles objects, places, animals or people in a mental image.
Episodic memoryEpisodic memory is the memory of everyday events (such as times, location geography, associated emotions, and other contextual information) that can be explicitly stated or conjured. It is the collection of past personal experiences that occurred at particular times and places; for example, the party on one's 7th birthday. Along with semantic memory, it comprises the category of explicit memory, one of the two major divisions of long-term memory (the other being implicit memory).
Neural networkA neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
RhythmRhythm (from Greek ῥυθμός, rhythmos, "any regular recurring motion, symmetry") generally means a "movement marked by the regulated succession of strong and weak elements, or of opposite or different conditions". This general meaning of regular recurrence or pattern in time can apply to a wide variety of cyclical natural phenomena having a periodicity or frequency of anything from microseconds to several seconds (as with the riff in a rock music song); to several minutes or hours, or, at the most extreme, even over many years.
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.
