3,363 research outputs found
Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights
Significant success has been reported recently using deep neural networks for
classification. Such large networks can be computationally intensive, even
after training is over. Implementing these trained networks in hardware chips
with a limited precision of synaptic weights may improve their speed and energy
efficiency by several orders of magnitude, thus enabling their integration into
small and low-power electronic devices. With this motivation, we develop a
computationally efficient learning algorithm for multilayer neural networks
with binary weights, assuming all the hidden neurons have a fan-out of one.
This algorithm, derived within a Bayesian probabilistic online setting, is
shown to work well for both synthetic and real-world problems, performing
comparably to algorithms with real-valued weights, while retaining
computational tractability
A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data
Deducing the structure of neural circuits is one of the central problems of
modern neuroscience. Recently-introduced calcium fluorescent imaging methods
permit experimentalists to observe network activity in large populations of
neurons, but these techniques provide only indirect observations of neural
spike trains, with limited time resolution and signal quality. In this work we
present a Bayesian approach for inferring neural circuitry given this type of
imaging data. We model the network activity in terms of a collection of coupled
hidden Markov chains, with each chain corresponding to a single neuron in the
network and the coupling between the chains reflecting the network's
connectivity matrix. We derive a Monte Carlo Expectation--Maximization
algorithm for fitting the model parameters; to obtain the sufficient statistics
in a computationally-efficient manner, we introduce a specialized
blockwise-Gibbs algorithm for sampling from the joint activity of all observed
neurons given the observed fluorescence data. We perform large-scale
simulations of randomly connected neuronal networks with biophysically
realistic parameters and find that the proposed methods can accurately infer
the connectivity in these networks given reasonable experimental and
computational constraints. In addition, the estimation accuracy may be improved
significantly by incorporating prior knowledge about the sparseness of
connectivity in the network, via standard L penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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