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

The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information

After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation), collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside–outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of “sleep replay” of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata