Attractor neural networks storing multiple space representations: a model for hippocampal place fields

A recurrent neural network model storing multiple spatial maps, or ``charts'', is analyzed. A network of this type has been suggested as a model for the origin of place cells in the hippocampus of rodents. The extremely diluted and fully connected limits are studied, and the storage capacity and the information capacity are found. The important parameters determining the performance of the network are the sparsity of the spatial representations and the degree of connectivity, as found already for the storage of individual memory patterns in the general theory of auto-associative networks. Such results suggest a quantitative parallel between theories of hippocampal function in different animal species, such as primates (episodic memory) and rodents (memory for space).Comment: 19 RevTeX pages, 8 pes figure

Sequential Reinstatement of Neocortical Activity during Slow Oscillations Depends on Cells’ Global Activity

During Slow Wave Sleep (SWS), cortical activity is dominated by endogenous processes modulated by slow oscillations (0.1–1 Hz): cell ensembles fluctuate between states of sustained activity (UP states) and silent epochs (DOWN states). We investigate here the temporal structure of ensemble activity during UP states by means of multiple single unit recordings in the prefrontal cortex of naturally sleeping rats. As previously shown, the firing rate of each PFC cell peaks at a distinct time lag after the DOWN/UP transition in a consistent order. We show here that, conversely, the latency of the first spike after the UP state onset depends primarily on the session-averaged firing rates of cells (which can be considered as an indirect measure of their intrinsic excitability). This latency can be explained by a simple homogeneous process (Poisson model) of cell firing, with sleep averaged firing rates employed as parameters. Thus, at DOWN/UP transitions, neurons are affected both by a slow process, possibly originating in the cortical network, modulating the time course of firing for each cell, and by a fast, relatively stereotyped reinstatement of activity, related mostly to global activity levels

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

Open source tools for large-scale neuroscience

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience

Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning

To study the interplay between hippocampus and medial prefrontal cortex (Pfc) and its importance for learning and memory consolidation, we measured the coherence in theta oscillations between these two structures in rats learning new rules on a Y maze. Coherence peaked at the choice point, most strongly after task rule acquisition. Simultaneously, Pfc pyramidal neurons reorganized their phase, concentrating at hippocampal theta trough, and synchronous cell assemblies emerged. This synchronous state may result from increased inhibition exerted by interneurons on pyramidal cells, as measured by cross-correlation, and could be modulated by dopamine: we found similar hippocampal-Pfc theta coherence increases and neuronal phase shifts following local administration of dopamine in Pfc of anesthetized rats. Pfc cell assemblies emerging during high coherence were preferentially replayed during subsequent sleep, concurrent with hippocampal sharp waves. Thus, hippocampal/prefrontal coherence could lead to synchronization of reward predicting activity in prefrontal networks, tagging it for subsequent memory consolidation.European Commission (Contract FP6-IST 027819)European Commission (Contract FP6-IST-027140)European Commission (Contract FP6-IST-027017

Light sleep versus slow wave sleep in memory consolidation:a question of global versus local processes?

Contains fulltext : 135488.pdf (publisher's version ) (Closed access)Sleep is strongly involved in memory consolidation, but its role remains unclear. 'Sleep replay', the active potentiation of relevant synaptic connections via reactivation of patterns of network activity that occurred during previous experience, has received considerable attention. Alternatively, sleep has been suggested to regulate synaptic weights homeostatically and nonspecifically, thereby improving the signal:noise ratio of memory traces. Here, we reconcile these theories by highlighting the distinction between light and deep nonrapid eye movement (NREM) sleep. Specifically, we draw on recent studies to suggest a link between light NREM and active potentiation, and between deep NREM and homeostatic regulation. This framework could serve as a key for interpreting the physiology of sleep stages and reconciling inconsistencies in terminology in this field

Forward fitting STIX visibilities

Aims. We seek to determine to what extent the problem of forward fitting visibilities measured by the Spectrometer/Telescope Imaging X-rays (STIX) on board Solar Orbiter becomes more challenging with respect to the same problem in the case of previous hard X-ray solar imaging missions. In addition, we aim to identify an effective optimization scheme for parametric imaging for STIX. Methods. This paper introduces a global search optimization for forward-fitting STIX visibilities and compares its effectiveness with respect to the standard simplex-based optimization used so far for the analysis of visibilities measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We made this comparison by considering experimental visibilities measured by both RHESSI and STIX, as weel as synthetic visibilities generated by accounting for the STIX signal formation model. Results. We found that among the three global search algorithms for parametric imaging, particle swarm optimization (PSO) exhibits the best performances in terms of both stability and computational effectiveness. This method is as reliable as the simplex method in the case of RHESSI visibilities. However, PSO is significantly more robust when applied to STIX simulated and experimental visibilities. Conclusions. A standard optimization based on local search of minima is not effective enough for forward-fitting the few visibilities sampled by STIX in the spatial frequency plane. Therefore, more sophisticated optimization schemes based on global search must be introduced for parametric imaging in the case of the Solar Orbiter X-ray telescope. The forward-fitting routine based on PSO proved to be significantly robust and reliable, and it could be considered as an effective candidate tool for parametric imaging in the STIX context

JAK3/STAT5/6 Pathway Alterations Are Associated with Immune Deviation in CD8+ T Cells in Renal Cell Carcinoma Patients

To investigate the molecular mechanisms underlying altered T cell response in renal cell carcinoma (RCC) patients, we compared autologous and allogeneic CD8+ T cell responses against RCC line from RCC patients and their HLA-matched donors, using mixed lymphocyte/tumor cell cultures (MLTCs). In addition, we analyzed the expression of molecules associated with cell cycle regulation. Autologous MLTC responder CD8+ T cells showed cytotoxic activity against RCC cell lines; however the analysis of the distribution of CD8+ T-cell subsets revealed that allogenic counterparts mediate superior antitumor efficacy. In RCC patients, a decreased proliferative response to tumor, associated with defects in JAK3/STAT5/6 expression that led to increased p27KIP1 expression and alterations in the cell cycle, was observed. These data define a molecular pathway involved in cell cycle regulation that is associated with the dysfunction of tumor-specific CD8+ effector cells. If validated, this may define a therapeutic target in the setting of patients with RCC

Photon Management in Two-Dimensional Disordered Media

Elaborating reliable and versatile strategies for efficient light coupling between free space and thin films is of crucial importance for new technologies in energy efficiency. Nanostructured materials have opened unprecedented opportunities for light management, notably in thin-film solar cells. Efficient coherent light trapping has been accomplished through the careful design of plasmonic nanoparticles and gratings, resonant dielectric particles and photonic crystals. Alternative approaches have used randomly-textured surfaces as strong light diffusers to benefit from their broadband and wide-angle properties. Here, we propose a new strategy for photon management in thin films that combines both advantages of an efficient trapping due to coherent optical effects and broadband/wide-angle properties due to disorder. Our approach consists in the excitation of electromagnetic modes formed by multiple light scattering and wave interference in two-dimensional random media. We show, by numerical calculations, that the spectral and angular responses of thin films containing disordered photonic patterns are intimately related to the in-plane light transport process and can be tuned through structural correlations. Our findings, which are applicable to all waves, are particularly suited for improving the absorption efficiency of thin-film solar cells and can provide a novel approach for high-extraction efficiency light-emitting diodes