Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity

arXiv.org Machine Learning

As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established to advance the objectivization of AD diagnosis and progression assessment. Here, we utilize Bayesian neural networks to develop a multivariate predictor for AD severity using a wide range of quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both automatically controls model complexity and provides a predictive distribution over the target function, giving uncertainty bounds for our regression task. It is therefore well suited to clinical neuroscience, where data sets are typically sparse and practitioners require a precise assessment of the predictive uncertainty. We use data of one of the largest prospective AD EEG trials ever conducted to demonstrate the potential of Bayesian deep learning in this domain, while comparing two distinct Bayesian neural network approaches, i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.


PyData Carolinas 2016 Presentation: Deep Finch? A Continued Comparison of Machine Learning Models to Label Birdsong Syllables

#artificialintelligence

Songbirds provide a model system that neuroscientists use to understand how the brain learns and controls speech and similar skills. Much like infants learning to speak from their parents, songbirds learn their song from a tutor and practice it millions of times before reaching maturity. Also like humans, songbirds have evolved special brain regions for learning and producing their vocalizations. These newly-evolved brain regions in songbirds, known as the song system, are found within broader brain areas shared by birds and humans across evolution. So by studying how the song system works, we can learn about our own brains.


Improving Variational Auto-Encoders using Householder Flow

arXiv.org Machine Learning

Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distribution. One fashion of enriching the variational posterior distribution is application of normalizing flows, i.e., a series of invertible transformations to latent variables with a simple posterior. In this paper, we follow this line of thinking and propose a volume-preserving flow that uses a series of Householder transformations. We show empirically on MNIST dataset and histopathology data that the proposed flow allows to obtain more flexible variational posterior and competitive results comparing to other normalizing flows.


Neuroscientists Transform Brain Activity to Speech with AI

#artificialintelligence

Artificial intelligence is enabling many scientific breakthroughs, especially in fields of study that generate high volumes of complex data such as neuroscience. As impossible as it may seem, neuroscientists are making strides in decoding neural activity into speech using artificial neural networks. Yesterday, the neuroscience team of Gopala K. Anumanchipalli, Josh Chartier, and Edward F. Chang of University of California San Francisco (UCSF) published in Nature their study using artificial intelligence and a state-of-the-art brain-machine interface to produce synthetic speech from brain recordings. The concept is relatively straightforward--record the brain activity and audio of participants while they are reading aloud in order to create a system that decodes brain signals for vocal tract movements, then synthesize speech from the decoded movements. The execution of the concept required sophisticated finessing of cutting-edge AI techniques and tools.


Nonlinear Markov Random Fields Learned via Backpropagation

arXiv.org Machine Learning

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.