4,908 research outputs found
Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis
Electrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas
From receptive profiles to a metric model of V1
In this work we show how to construct connectivity kernels induced by the
receptive profiles of simple cells of the primary visual cortex (V1). These
kernels are directly defined by the shape of such profiles: this provides a
metric model for the functional architecture of V1, whose global geometry is
determined by the reciprocal interactions between local elements. Our
construction adapts to any bank of filters chosen to represent a set of
receptive profiles, since it does not require any structure on the
parameterization of the family. The connectivity kernel that we define carries
a geometrical structure consistent with the well-known properties of long-range
horizontal connections in V1, and it is compatible with the perceptual rules
synthesized by the concept of association field. These characteristics are
still present when the kernel is constructed from a bank of filters arising
from an unsupervised learning algorithm.Comment: 25 pages, 18 figures. Added acknowledgement
Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality
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