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Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble.
Implicit about the decoding hypothesis is the assumption that neural spiking in the brain somehow represents stimuli in the external world. The decoding of neural data would be impossible if the neurons were firing randomly: nothing would be represented. This process of decoding neural data forms a loop with neural encoding. First, the organism ...
Statistical language acquisition, a branch of developmental psycholinguistics, studies the process by which humans develop the ability to perceive, produce, comprehend, and communicate with natural language in all of its aspects (phonological, syntactic, lexical, morphological, semantic) through the use of general learning mechanisms operating on statistical patterns in the linguistic input.
Surface of the human brain, with Brodmann areas numbered An image of neural pathways in the brain taken using diffusion tensor imaging. Neurolinguistics is the study of neural mechanisms in the human brain that control the comprehension, production, and acquisition of language.
Analyzing actual neural system in response to natural images In a report in Science from 2000, William E. Vinje and Jack Gallant outlined a series of experiments used to test elements of the efficient coding hypothesis, including a theory that the non-classical receptive field (nCRF) decorrelates projections from the primary visual cortex .
Semantic spaces [note 1] [1] in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can ...
Much of the early work that applied a predictive coding framework to neural mechanisms came from sensory processing, particularly in the visual cortex. [ 3 ] [ 12 ] These theories assume that the cortical architecture can be divided into hierarchically stacked levels, which correspond to different cortical regions.
If a word meaning is grounded in the visual shapes of the objects, the word form circuit is active together with neural activity in the ventral-temporal visual stream related to processing of visual object information. Correlation learning links the word and object circuits, resulting in an embodied object-semantic relationship. To study the ...