Flexible and accurate inference and learning for deep generative models

We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but unlike adverserial methods) our approach learns an explicit inference or "recognition" model to approximate the posterior distribution over the latent variables. Unlike in these earlier methods, the posterior representation is not limited to a narrow tractable parameterised form (nor is it represented by samples). To train the generative and recognition models we develop an extended wake-sleep algorithm inspired by the original Helmholtz Machine. This makes it possible to learn hierarchical latent models with both discrete and continuous variables, where an accurate posterior representation is essential. We demonstrate that the new algorithm outperforms current state-of-the-art methods on synthetic, natural image patch and the MNIST data sets

Probabilistic learning and computation in brains and machines

Humans and animals are able to solve a wide variety of perceptual, decision making and motor tasks with great exibility. Moreover, behavioural evidence shows that this exibility extends to situations where accuracy requires the correct treatment of uncertainty induced by noise and ambiguity in the available sensory information as well as noise internal to the brain. It has been suggested that this adequate handling of uncertainty is based on a learned internal model, e.g. in the case of perception, a generative model of sensory observations. Learning latent variable models and performing inference in them is a key challenge for both biological and arti cial learning systems. Here, we introduce a new approach to learning in hierarchical latent variable models called the Distributed Distributional Code Helmholtz Machine (DDC-HM), which emphasises exibility and accuracy in the inferential process. The approximate posterior over unobserved variables is represented implicitly as a set of expectations, corresponding to mean parameters of an exponential family distribution. To train the generative and recognition models we develop an extended wake-sleep algorithm inspired by the original Helmholtz Machine. As a result, the DDC-HM is able to learn hierarchical latent models without having to propagate gradients across di erent stochastic layers|making our approach biologically appealing. In the second part of the thesis, we review existing proposals for neural representations of uncertainty with a focus on representational and computational exibility as well as experimental support. Finally, we consider inference and learning in dynamical environment models using Distributed Distributional Codes to represent both the stochastic latent transition model and the inferred posterior distributions. We show that this model makes it possible to generalise successor representations to biologically more realistic, partially observed settings

Neural networks with small-world topology are optimal for encoding based on spatiotemporal patterns of spikes

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Restricted Energy Transfer in Laser Desorption of High Molecular Weight Biomolecules

Producing ions from large molecules is of distinguished importance in mass spectrometry. In our present study we survey different laser desorption methods in view of their virtues and drawbacks in volatilization and ion generation. Laser induced thermal desorption and matrix assisted laser desorption are assessed with special emphasis to the recent breakthrough in the field (m/z \u3e 100,000 ions produced by matrix assisted laser desorption). Efforts to understand and describe laser desorption and ionization are also reported. We emphasize the role of restricted energy transfer pathways as a possible explanation to the volatilization of non-degraded large molecules

A neurally plausible model learns successor representations in partially observable environments

Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are often not directly observable but must be inferred using available sensory information. Successor representations (SR) have been proposed as a middle-ground between model-based and model-free reinforcement learning strategies, allowing for fast value computation and rapid adaptation to changes in the reward function or goal locations. Indeed, recent studies suggest that features of neural responses are consistent with the SR framework. However, it is not clear how such representations might be learned and computed in partially observed, noisy environments. Here, we introduce a neurally plausible model using distributional successor features, which builds on the distributed distributional code for the representation and computation of uncertainty, and which allows for efficient value function computation in partially observed environments via the successor representation. We show that distributional successor features can support reinforcement learning in noisy environments in which direct learning of successful policies is infeasible

Memory Consolidation in Sleep Dream or Reality

AbstractWe discuss several lines of evidence refuting the hypothesis that procedural or declarative memories are processed/consolidated in sleep. One of the strongest arguments against a role for sleep in declarative memory involves the demonstration that the marked suppression or elimination of REM sleep in subjects on antidepressant drugs or with brainstem lesions produces no detrimental effects on cognition. Procedural memory, like declarative memory, undergoes a slow, time-dependent period of consolidation. A process has recently been described wherein performance on some procedural tasks improves with the mere passage of time and has been termed “enhancement.” Some studies, but not others, have reported that the consolidation/enhancement of perceptual and motor skills is dependent on sleep. We suggest that consolidation or enhancement, initiated in waking with task acquisition, could in some instances extend to sleep, but sleep would serve no unique role in these processes. In sum, there is no compelling evidence to support a relationship between sleep and memory consolidation

Atmospheric-pressure Molecular Imaging of Biological Tissues and Biofilms by LAESI Mass Spectrometry

Ambient ionization methods in mass spectrometry allow analytical investigations to be performed directly on a tissue or biofilm under native-like experimental conditions. Laser ablation electrospray ionization (LAESI) is one such development and is particularly well-suited for the investigation of water-containing specimens. LAESI utilizes a mid-infrared laser beam (2.94 μm wavelength) to excite the water molecules of the sample. When the ablation fluence threshold is exceeded, the sample material is expelled in the form of particulate matter and these projectiles travel to tens of millimeters above the sample surface. In LAESI, this ablation plume is intercepted by highly charged droplets to capture a fraction of the ejected sample material and convert its chemical constituents into gas-phase ions. A mass spectrometer equipped with an atmospheric-pressure ion source interface is employed to analyze and record the composition of the released ions originating from the probed area (pixel) of the sample. A systematic interrogation over an array of pixels opens a way for molecular imaging in the microprobe analysis mode. A unique aspect of LAESI mass spectrometric imaging is depth profiling that, in combination with lateral imaging, enables three-dimensional (3D) molecular imaging. With current lateral and depth resolutions of ~100 μm and ~40 μm, respectively, LAESI mass spectrometric imaging helps to explore the molecular structure of biological tissues. Herein, we review the major elements of a LAESI system and provide guidelines for a successful imaging experiment

Oxidation on the Nickel Hydroxide Electrode

It has been shown that in alkaline solution alcoholic hydroxyl is oxidized by the charged nickel hydroxide electrode - similarly as by oxidation in the presence of nickel salt catalysts or by »nickel peroxide« - to carboxylic acid. An electrochemical method has been devised for the study of the reaction rate, based on the potentiometric indication of the depletion of NiOOH. It has been shown that the reaction rate is proportional to the amount of NiOOH and the concentration of the alcohol, but independent of the hydroxide ion concentration and the electrode potential. An electrochemical procedure has been devised for the practical implementation of oxidation on NiOOH. In this way a number of primary alcohols may be oxidized with good yields. It has been shown that the oxidation of the vitamin C intermediate di-0-isopropylidene-sorbose can be performed electrochemically with yields above 950/o and, according to estimates, economically on an industrial scale