منابع مشابه
Learning and receptive field plasticity.
The cerebral cortex has long been known to play a central role in the storage and retrieval of long-term memories. Therefore it would be expected that mechanisms of cortical plasticity underlying information storage should be found in the cortex of adult animals, and they should be manifest as an alteration in the functional specificity of cells, in the functional architecture of cortex, and in...
متن کاملLearning-Induced Receptive Field Plasticity in the Primary Auditory Cortex
Primary sensory cortex in the adult is modified by learning. The primary auditory cortex is retuned when a tone is paired with a behaviorally relevant reinforcer. Frequency receptive fields are shifted toward or to the frequency of the signal stimulus, yielding enhanced processing and representation of important frequencies. Receptive field plasticity constitutes ‘‘physiological memory’’ becaus...
متن کاملSupplementary Material: Unsupervised learning models of primary cortical receptive fields and receptive field plasticity
Independent component analysis (ICA) The ICA algorithm has been applied successfully to modeling V1 simple cell receptive fields [1, 2]. It is closely related to sparse coding methods, and can be cast in terms of a simple generative model [3]: We suppose that our data x ∈ R is an unknown linear mixture of independent, non-Gaussian sources, i.e. x = As where A ∈ Rn×n is unknown. During learning,...
متن کاملUnsupervised learning models of primary cortical receptive fields and receptive field plasticity
The efficient coding hypothesis holds that neural receptive fields are adapted to the statistics of the environment, but is agnostic to the timescale of this adaptation, which occurs on both evolutionary and developmental timescales. In this work we focus on that component of adaptation which occurs during an organism’s lifetime, and show that a number of unsupervised feature learning algorithm...
متن کاملCollaborative Receptive Field Learning
The challenge of object categorization in images is largely due to arbitrary translations and scales of the foreground objects. To attack this difficulty, we propose a new approach called collaborative receptive field learning to extract specific receptive fields (RF’s) or regions from multiple images, and the selected RF’s are supposed to focus on the foreground objects of a common category. T...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 1996
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.93.20.10546