Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators

We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community

Metabotropic action of postsynaptic kainate receptors triggers hippocampal long-term potentiation

Long-term potentiation (LTP) in the rat hippocampus is the most extensively studied cellular model for learning and memory. Induction of classical LTP involves an NMDA receptor- and calcium-dependent increase in functional synaptic AMPA receptors mediated by enhanced recycling of internalized AMPA receptors back to the postsynaptic membrane. Here we report a novel, physiologically relevant NMDA receptor-independent mechanism that drives increased AMPA receptor recycling and LTP. This pathway requires the metabotropic action of kainate receptors and activation of G-protein, protein kinase C and phospholipase C. Like classical LTP, kainate receptor-dependent LTP recruits recycling endosomes to spines, enhances synaptic recycling of AMPA receptors to increase their surface expression and elicits structural changes in spines, including increased growth and maturation. These data reveal a new and previously unsuspected role for postsynaptic kainate receptors in the induction of functional and structural plasticity in the hippocampus

Progress in methodology for probabilistic assessment of accidents: timing of accident sequences

SIGLEAvailable from CEN Saclay, Service de Documentation, 91191 Gif-sur-Yvette Cedex (France) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

An innovative compilation tool-chain for embedded multi-core architectures

In this paper, we propose a compilation tool-chain supporting the eective exploitation of multi-core architectures oering hundreds of cores. The tool-chain leverages on both the application requirements and the platform-specic features to provide developers with a powerful parallel-programming environment able to generate ecient parallel code. The design of parallel applications follows a semi-automatic approach enabling the programmer to transfer to back-end tools platform-specic code generation and optimization, thus making possible to avoid the clobbering of code with non-portable and complex directives. The programmer can graphically parallelize the application (mainly data-streaming ones) for the target platform using Thales' Spear Design Environment. The resulting parallelization is generated under the form of an Intermediate Representation, which is then passed to the back-end tools (HPC Project's Par4All) that generates efficient target code. We present the results obtained parallelizing a small subset of the RT-STAP radar algorithm and the Chirp filltering algorithm on standard multi-core and on nVidia GPUs