8,048 research outputs found
Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop
edge-like receptive fields. One approach to understanding the emergence of this
response is to state that neural activity has to efficiently represent sensory
data with respect to the statistics of natural scenes. Furthermore, it is
believed that such an efficient coding is achieved using a competition across
neurons so as to generate a sparse representation, that is, where a relatively
small number of neurons are simultaneously active. Indeed, different models of
sparse coding, coupled with Hebbian learning and homeostasis, have been
proposed that successfully match the observed emergent response. However, the
specific role of homeostasis in learning such sparse representations is still
largely unknown. By quantitatively assessing the efficiency of the neural
representation during learning, we derive a cooperative homeostasis mechanism
that optimally tunes the competition between neurons within the sparse coding
algorithm. We apply this homeostasis while learning small patches taken from
natural images and compare its efficiency with state-of-the-art algorithms.
Results show that while different sparse coding algorithms give similar coding
results, the homeostasis provides an optimal balance for the representation of
natural images within the population of neurons. Competition in sparse coding
is optimized when it is fair. By contributing to optimizing statistical
competition across neurons, homeostasis is crucial in providing a more
efficient solution to the emergence of independent components
A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons
We propose a scalable semiparametric Bayesian model to capture dependencies
among multiple neurons by detecting their co-firing (possibly with some lag
time) patterns over time. After discretizing time so there is at most one spike
at each interval, the resulting sequence of 1's (spike) and 0's (silence) for
each neuron is modeled using the logistic function of a continuous latent
variable with a Gaussian process prior. For multiple neurons, the corresponding
marginal distributions are coupled to their joint probability distribution
using a parametric copula model. The advantages of our approach are as follows:
the nonparametric component (i.e., the Gaussian process model) provides a
flexible framework for modeling the underlying firing rates; the parametric
component (i.e., the copula model) allows us to make inference regarding both
contemporaneous and lagged relationships among neurons; using the copula model,
we construct multivariate probabilistic models by separating the modeling of
univariate marginal distributions from the modeling of dependence structure
among variables; our method is easy to implement using a computationally
efficient sampling algorithm that can be easily extended to high dimensional
problems. Using simulated data, we show that our approach could correctly
capture temporal dependencies in firing rates and identify synchronous neurons.
We also apply our model to spike train data obtained from prefrontal cortical
areas in rat's brain
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