23,911 research outputs found
A computer vision model for visual-object-based attention and eye movements
This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda-
tion of Chin
The emergence of choice: Decision-making and strategic thinking through analogies
Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud
how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud
may evolve
Cortical region interactions and the functional role of apical dendrites
The basal and distal apical dendrites of pyramidal cells occupy distinct
cortical layers and are targeted by axons originating in different cortical
regions. Hence, apical and basal dendrites receive information from distinct
sources. Physiological evidence suggests that this anatomically observed
segregation of input sources may have functional significance. This possibility
has been explored in various connectionist models that employ neurons with
functionally distinct apical and basal compartments. A neuron in which separate
sets of inputs can be integrated independently has the potential to operate in a
variety of ways which are not possible for the conventional model of a neuron in
which all inputs are treated equally. This article thus considers how
functionally distinct apical and basal dendrites can contribute to the
information processing capacities of single neurons and, in particular, how
information from different cortical regions could have disparate affects on
neural activity and learning
A feedback model of visual attention
Feedback connections are a prominent feature of cortical anatomy and are likely
to have significant functional role in neural information processing. We present
a neural network model of cortical feedback that successfully simulates
neurophysiological data associated with attention. In this domain our model can
be considered a more detailed, and biologically plausible, implementation of the
biased competition model of attention. However, our model is more general as it
can also explain a variety of other top-down processes in vision, such as
figure/ground segmentation and contextual cueing. This model thus suggests that
a common mechanism, involving cortical feedback pathways, is responsible for a
range of phenomena and provides a unified account of currently disparate areas
of research
A Biologically Plausible Learning Rule for Deep Learning in the Brain
Researchers have proposed that deep learning, which is providing important
progress in a wide range of high complexity tasks, might inspire new insights
into learning in the brain. However, the methods used for deep learning by
artificial neural networks are biologically unrealistic and would need to be
replaced by biologically realistic counterparts. Previous biologically
plausible reinforcement learning rules, like AGREL and AuGMEnT, showed
promising results but focused on shallow networks with three layers. Will these
learning rules also generalize to networks with more layers and can they handle
tasks of higher complexity? We demonstrate the learning scheme on classical and
hard image-classification benchmarks, namely MNIST, CIFAR10 and CIFAR100, cast
as direct reward tasks, both for fully connected, convolutional and locally
connected architectures. We show that our learning rule - Q-AGREL - performs
comparably to supervised learning via error-backpropagation, with this type of
trial-and-error reinforcement learning requiring only 1.5-2.5 times more
epochs, even when classifying 100 different classes as in CIFAR100. Our results
provide new insights into how deep learning may be implemented in the brain
STNet: Selective Tuning of Convolutional Networks for Object Localization
Visual attention modeling has recently gained momentum in developing visual
hierarchies provided by Convolutional Neural Networks. Despite recent successes
of feedforward processing on the abstraction of concepts form raw images, the
inherent nature of feedback processing has remained computationally
controversial. Inspired by the computational models of covert visual attention,
we propose the Selective Tuning of Convolutional Networks (STNet). It is
composed of both streams of Bottom-Up and Top-Down information processing to
selectively tune the visual representation of Convolutional networks. We
experimentally evaluate the performance of STNet for the weakly-supervised
localization task on the ImageNet benchmark dataset. We demonstrate that STNet
not only successfully surpasses the state-of-the-art results but also generates
attention-driven class hypothesis maps
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.Comment: corrected typo in abstrac
- …