منابع مشابه
Receptive Field Encoding Model for Dynamic Natural Vision
Introduction: Encoding models are used to predict human brain activity in response to sensory stimuli. The purpose of these models is to explain how sensory information represent in the brain. Convolutional neural networks trained by images are capable of encoding magnetic resonance imaging data of humans viewing natural images. Considering the hemodynamic response function, these networks are ...
متن کاملSystem Analysis and Receptive Field
0 K(τ)s(t− τ)dτ, (1) where s(t) is the input signal, r(t) is the output signal, and K(·) is the linear kernel of the system that can be measured by using a Dirac-δ function δ(·) or any type of white noise (not necessarily gaussian) as the input signal. We take the convolution in Eq. (1) starting from 0 instead of −∞ in order to satisfy the causality condition: the input signal in the future wil...
متن کاملLearning 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 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 کاملSelf-Organization of Color Receptive Fields based on the Infomax Principle
Various self-organization models with Hebb-type unsupervised learning have been proposed to explain the functional role of retinal or cortical neuron. In unsupervised learning paradigm, unlike supervised learning such as BP which needs desired outputs, desired property of the representation to be learned is provided. Infomax principle (Linsker, 1989) is an information-theoretic objection for pe...
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: The Brain & Neural Networks
سال: 2002
ISSN: 1340-766X,1883-0455
DOI: 10.3902/jnns.9.224