The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Identifying and classifying biomedical perturbations in text

Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene–phenotype associations and protein–protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems

Olfactory learning in Drosophila

Animals are able to form associative memories and benefit from past experience. In classical conditioning an animal is trained to associate an initially neutral stimulus by pairing it with a stimulus that triggers an innate response. The neutral stimulus is commonly referred to as conditioned stimulus (CS) and the reinforcing stimulus as unconditioned stimulus (US). The underlying neuronal mechanisms and structures are an intensely investigated topic. The fruit fly Drosophila melanogaster is a prime model animal to investigate the mechanisms of learning. In this thesis we propose fundamental circuit motifs that explain aspects of aversive olfactory learning as it is observed in the fruit fly. Changing parameters of the learning paradigm affects the behavioral outcome in different ways. The relative timing between CS and US affects the hedonic value of the CS. Reversing the order changes the behavioral response from conditioned avoidance to conditioned approach. We propose a timing-dependent biochemical reaction cascade, which can account for this phenomenon. In addition to form odor-specific memories, flies are able to associate a specific odor intensity. In aversive olfactory conditioning they show less avoidance to lower and higher intensities of the same odor. However the layout of the first two olfactory processing layers does not support this kind of learning due to a nested representation of odor intensity. We propose a basic circuit motif that transforms the nested monotonic intensity representation to a non-monotonic representation that supports intensity specific learning. Flies are able to bridge a stimulus free interval between CS and US to form an association. It is unclear so far where the stimulus trace of the CS is represented in the fly's nervous system. We analyze recordings from the first three layers of olfactory processing with an advanced machine learning approach. We argue that third order neurons are likely to harbor the stimulus trace

Maintenance of aversive memories shown by fear extinction-impaired phenotypes is associated with increased activity in the amygdaloid-prefrontal circuit

Although aversive memory has been mainly addressed by analysing the changes occurring in average populations, the study of neuronal mechanisms of outliers allows understanding the involvement of individual differences in fear conditioning and extinction. We recently developed an innovative experimental model of individual differences in approach and avoidance behaviors, classifying the mice as Approaching, Balancing or Avoiding animals according to their responses to conflicting stimuli. The approach and avoidance behaviors appear to be the primary reactions to rewarding and threatening stimuli and may represent predictors of vulnerability (or resilience) to fear. We submitted the three mice phenotypes to Contextual Fear Conditioning. In comparison to Balancing animals, Approaching and Avoiding mice exhibited no middle- or long-term fear extinction. The two non-extinguishing phenotypes exhibited potentiated glutamatergic neurotransmission (spontaneous excitatory postsynaptic currents/spinogenesis) of pyramidal neurons of medial prefrontal cortex and basolateral amygdala. Basing on the a priori individuation of outliers, we demonstrated that the maintenance of aversive memories is linked to increased spinogenesis and excitatory signaling in the amygdala-prefrontal cortex fear matri