What is neurorepresentationalism?:From neural activity and predictive processing to multi-level representations and consciousness

This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body (Pennartz, 2015)

Gene expression analysis of neuronal precursors from adult mouse brain and differential screen for neural stem cell markers

In the adult mouse brain, neuronal precursor cells continuously emanate from neural stem cells (NSC) in the subventricular zone (SVZ) and migrate into the olfactory bulb (OB) where they differentiate to serve as replenishment for GABAergic interneurons. During the migration process, PSA-NCAM (Polysialic acid-Neural cell adhesion molecule) specifically marks the neuronal precursors (PSA+ cells). This phenomenon was exploited in the framework of this doctoral thesis to isolate a homogeneous cell population of neuronal precursor cells using Fluorescence-activated cell sorting. Here, the first comprehensive picture of the gene expression in PSA+ precursors was generated using Serial Analysis of Gene Expression (SAGE). Comparison of SAGE data for PSA+ cells and for adult total brain (ATB) led to the identification of precursor-enriched genes. For selected genes, the results were validated using cDNA microarrays and quantitative real-time PCR, and the expression was analyzed at the cellular level in mouse brain using in situ hybridizations. Genes previously described in this context like the proliferation inhibitor CD24, the sialyltransferase STX and the Reelin receptor ApoER2 confirmed the identity of the precursor cells and the accuracy of the SAGE. Individual characterized genes that were so far unknown in the PSA+ cell population were identified as well as functional groups of genes by means of cluster analysis of SAGE data. The presence of transcription factors of the Sox and Dlx families, Pax6 and Meis2 indicated that secondary neurogenesis might be largely controlled by the same factors that are active during development. Clusters for apoptosis and proliferation are both upregulated. The high expression of chemotactic factors in the neuronal precursors suggests that they might be involved in neuronal cell migration. In addition, novel genes like RIKEN 3110003A17 were observed. First functional data based on the SAGE are being generated in the framework of our collaboration with the Developmental Biology Institute of Marseille. Given that a lack of markers for NSC considerably impedes progress in NSC biology, the second part of this work aimed at identifying potential NSC markers by comparing SAGE data for embryonic stem (ES) cells, PSA+ cells and ATB. The selection strategy was based on two assumptions. First, in a hierarchical order of developmental potential, ES cells are positioned above NSC, which are above restricted precursors that in turn are above adult neurons and glia. Second, the genetic programs of ES cells and NSC overlap. Thus, genes that are highly expressed in ES cells and downregulated or absent in PSA+ neuronal precursors and ATB should in part also be expressed by the few stem cells in the adult brain. Eight candidates coding for cell surface proteins were identified from the resulting list of candidates and were investigated. Due to a public database mistake in situ hybridizations were performed for the glutamate transporter GLT1 and demonstrated expression in embryoid bodies, neurospheres and, strikingly, in the SVZ, the neurogenic area of the mouse forebrain. Taken together, this doctoral thesis generated the first gene expression profile for PSA+ neuronal precursors, which -together with the SAGE library for Bruce-4 ES cells- will serve as a starting basis for future functional analysis

A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs

Predictive coding has been argued as a mechanism underlying sensory processing in the brain. In computational models of predictive coding, the brain is described as a machine that constructs and continuously adapts a generative model based on the stimuli received from external environment. It uses this model to infer causes that generated the received stimuli. However, it is not clear how predictive coding can be used to construct deep neural network models of the brain while complying with the architectural constraints imposed by the brain. Here, we describe an algorithm to construct a deep generative model that can be used to infer causes behind the stimuli received from external environment. Specifically, we train a deep neural network on real-world images in an unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the causes inferred using the trained model on images of objects that are not used in training. Despite the novel features of these objects the model is able to infer the causes for them. Furthermore, the reconstructions of the original images obtained from the generative model using these inferred causes preserve important details of these objects

Assessing the commensurability of theories of consciousness:On the usefulness of common denominators in differentiating, integrating and testing hypotheses

How deep is the current diversity in the panoply of theories to define consciousness, and to what extent do these theories share common denominators? Here we first examine to what extent different theories are commensurable (or comparable) along particular dimensions. We posit logical (and, when applicable, empirical) commensurability as a necessary condition for identifying common denominators among different theories. By consequence, dimensions for inclusion in a set of logically and empirically commensurable theories of consciousness can be proposed. Next, we compare a limited subset of neuroscience-based theories in terms of commensurability. This analysis does not yield a denominator that might serve to define a minimally unifying model of consciousness. Theories that seem to be akin by one denominator can be remote by another. We suggest a methodology of comparing different theories via multiple probing questions, allowing to discern overall (dis)similarities between theories. Despite very different background definitions of consciousness, we conclude that, if attention is paid to the search for a common methological approach to brain-consciousness relationships, it should be possible in principle to overcome the current Babylonian confusion of tongues and eventually integrate and merge different theories