2,498 research outputs found
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
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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
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