125 research outputs found
Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons
Zebrafish pretectal neurons exhibit specificities for large-field optic flow
patterns associated with rotatory or translatory body motion. We investigate
the hypothesis that these specificities reflect the input statistics of natural
optic flow. Realistic motion sequences were generated using computer graphics
simulating self-motion in an underwater scene. Local retinal motion was
estimated with a motion detector and encoded in four populations of
directionally tuned retinal ganglion cells, represented as two signed input
variables. This activity was then used as input into one of two learning
networks: a sparse coding network (competitive learning) and backpropagation
network (supervised learning). Both simulations develop specificities for optic
flow which are comparable to those found in a neurophysiological study (Kubo et
al. 2014), and relative frequencies of the various neuronal responses are best
modeled by the sparse coding approach. We conclude that the optic flow neurons
in the zebrafish pretectum do reflect the optic flow statistics. The predicted
vectorial receptive fields show typical optic flow fields but also "Gabor" and
dipole-shaped patterns that likely reflect difference fields needed for
reconstruction by linear superposition.Comment: Published Conference Paper from ICANN 2018, Rhode
Nonstimulated early visual areas carry information about surrounding context
Even within the early sensory areas, the majority of the input to any given cortical neuron comes from other cortical neurons. To extend our knowledge of the contextual information that is transmitted by such lateral and feedback connections, we investigated how visually nonstimulated regions in primary visual cortex (V1) and visual area V2 are influenced by the surrounding context. We used functional magnetic resonance imaging (fMRI) and pattern-classification methods to show that the cortical representation of a nonstimulated quarter-field carries information that can discriminate the surrounding visual context. We show further that the activity patterns in these regions are significantly related to those observed with feed-forward stimulation and that these effects are driven primarily by V1. These results thus demonstrate that visual context strongly influences early visual areas even in the absence of differential feed-forward thalamic stimulation
Reconciling Predictive Coding and Biased Competition Models of Cortical Function
A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model
Sparse Coding and Autoencoders
In "Dictionary Learning" one tries to recover incoherent matrices (typically overcomplete and whose columns are assumed
to be normalized) and sparse vectors with a small
support of size for some while having access to observations
where . In this work we undertake a rigorous
analysis of whether gradient descent on the squared loss of an autoencoder can
solve the dictionary learning problem. The "Autoencoder" architecture we
consider is a mapping with a single
ReLU activation layer of size .
Under very mild distributional assumptions on , we prove that the norm
of the expected gradient of the standard squared loss function is
asymptotically (in sparse code dimension) negligible for all points in a small
neighborhood of . This is supported with experimental evidence using
synthetic data. We also conduct experiments to suggest that is a local
minimum. Along the way we prove that a layer of ReLU gates can be set up to
automatically recover the support of the sparse codes. This property holds
independent of the loss function. We believe that it could be of independent
interest.Comment: In this new version of the paper with a small change in the
distributional assumptions we are actually able to prove the asymptotic
criticality of a neighbourhood of the ground truth dictionary for even just
the standard squared loss of the ReLU autoencoder (unlike the regularized
loss in the older version
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