Abstract. Back-propagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment. This could become a serious issue as one considers deeper and more non-linear functions, e.g., consider the extreme case of nonlinearity where the relation between parameters and cost is actually discrete. Inspired by the biological implausibility of back-propagation, a few approaches have been proposed in the past that could play a similar credit assignment role. In this spirit, we explore a novel approach to credit assignment in deep networks that we call target propagation. The main idea is to compute targets rather than gradients, at each layer. Like gradients, they are propagated backwards. In a way that is related but different from previously proposed proxies for back-propagation which rely on a backwards network with symmetric weights, target propagation relies on auto-encoders at each layer. Unlike back-propagation, it can be applied even when units exchange stochastic bits rather than real numbers. We show that a linear correction for the imperfectness of the auto-encoders, called difference target propagation, is very effective to make target propagation actually work, leading to results comparable to back-propagation for deep networks with discrete and continuous units and denoising auto-encoders and achieving state of the art for stochastic networks.

Abstract. Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. The increase in computational power and the development of faster learning algorithms have made them applicable to relevant machine learning problems. They attracted much attention recently after being proposed as building blocks of multi-layer learning systems called deep belief networks. This tutorial introduces RBMs as undirected graphical models. The basic concepts of graphical models are introduced first, however, basic knowledge in statistics is presumed. Different learning algorithms for RBMs are discussed. As most of them are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and the required MCMC techniques is provided.

Ultrastructural analysis of Entamoeba histolytica reveals that this intestinal human pathogen lacks recognizable mitochondria, but the presence in its genome of genes encoding proteins of mitochondrial origin suggests the existence of a mitochondrially derived compartment. We have cloned the full‐length E. histolytica gene encoding one such protein, chaperonin CPN60, and have characterized its structure and expression. Using an affinity‐purified antibody raised against recombinant protein, we have localized native E. histolytica CPN60 to a previously undescribed organelle of putative mitochondrial origin, the mitosome. Most cells contain only one mitosome, as determined by immunofluorescence studies. Entamoeba histolytica CPN60 has an amino‐terminal extension reminiscent of known mitochondrial and hydrogenosomal targeting signals. Deletion of the first 15 amino acids of CPN60 leads to an accumulation of the truncated protein in the cytoplasm. However, this mutant phenotype can be reversed by replacement of the deleted amino acids with a mitochondrial targeting signal from Trypanosoma cruzi HSP70. The observed functional conservation between mitochondrial import in trypanosomes and mitosome import in Entamoeba is strong evidence that the E. histolytica organelle housing chaperonin CPN60 represents a mitochondrial remnant.

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The majority of public health interventions assessed are highly cost-effective. The next challenge is to provide commissioners with a framework that allows information from economic analyses to be combined with other criteria that supports making better investment decisions at a local level.

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