Learning Intuitive Physics and One-Shot Imitation Using State-Action-Prediction Self-Organizing Maps.

Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. Humans seem to learn rich representations by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks. We suggest a simple but effective unsupervised model which develops such characteristics.

Extraction of semantic biomedical relations from text using conditional random fields.

Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work.

Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference.

We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in the case of inconsistent qualitative knowledge. A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features.

Dynamic functional tuning of nonlinear cortical networks.

The mammalian neocortex is a highly complex and nonlinear dynamic system. One of its most prominent features is an omnipresent spontaneous neuronal activity. Here the possible functional role of this global background for cognitive flexibility is studied in a prototypic mean-field model area.

Learning to attend: modeling the shaping of selectivity in infero-temporal cortex in a categorization task.

Recent experiments on behaving monkeys have shown that learning a visual categorization task makes the neurons in infero-temporal cortex (ITC) more selective to the task-relevant features of the stimuli (Sigala and Logothetis in Nature 415 318-320, 2002). We hypothesize that such a selectivity modulation emerges from the interaction between ITC and other cortical area, presumably the prefrontal cortex (PFC), where the previously learned stimulus categories are encoded. We propose a biologically inspired model of excitatory and inhibitory spiking neurons with plastic synapses, modified according to a reward based Hebbian learning rule, to explain the experimental results and test the validity of our hypothesis.

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task.

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

Motivation: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level.

Modular biased-competition and cooperation: a candidate mechanism for selective working memory.

Prefrontal cortex (PFC) has been suggested to play an important role in executive cognitive functions, participating in planning and controlling behaviour. The results of several recent electrophysiological studies indicate that PFC might be involved not only in the active maintenance of information but in doing so in a context- or task-dependent manner. In a delayed-match-to-sample paradigm, recordings from neurons in the PFC showed their ability to selectively represent information, which is needed for task completion, suggesting that task-irrelevant information does not access working memory.

Hunting drug targets by systems-level modeling of gene expression profiles.

Structural learning of Bayesian networks applied to sets of genome-wide expression patterns has been recently discovered as a potentially useful tool for the systems-level statistical description of gene interactions. We train and analyze Bayesian networks with the goal of inferring biological aspects of gene function. Our two-component approach focuses on supporting the drug discovery process by identifying genes with central roles for the network operation, which could act as drug targets.

Mining functional modules in genetic networks with decomposable graphical models.

In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph.

Cooperation and biased competition model can explain attentional filtering in the prefrontal cortex.

Recent neurophysiological experimental results suggest that the prefrontal cortex plays an important role in filtering out unattended visual inputs. Here we propose a neurodynamical computational model of a part of the prefrontal cortex to account for the neural mechanisms defining this attentional filtering effect. Similar models have been employed to explain experimental results obtained during the performance of attention and working memory tasks.

Modeling the link between functional imaging and neuronal activity: synaptic metabolic demand and spike rates.

Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) measurements reflect changes in the hemodynamics which are thought to be related to local synaptic input to neuron populations. The local neuronal spiking activity, which is believed to form the basis of neuronal coding and communication, is not directly reflected in fMRI/PET measurements. We used a mean-field neuronal model of recurrently coupled excitatory and inhibitory neuronal populations to characterize the relationship between the synaptic activity (reflected in the PET and fMRI measurements) and the neuronal spike rates, averaged over brain areas.