Deliverables
The FACETS project is currently in month 231 (total project time: 48 months plus an expected extension by 12 months).These completed deliverables are publicly available:
| Deliverable | month/ name | |
|---|---|---|
| Report on modelling LFP and optical imaging signals. | 54 D5-5 |
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| Report on Functional consequences of Plasticity models | 48 D4-6 |
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| Manuscript on Information processing in random vs patchy layered networks | 48 D9-18 |
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| Next public release of PyNN/FacetsML | 45 M8-4 |
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| Report on the Mathematical aspects of single neuron models | 42 D4-5 |
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| Report on the application of a two-variable model (e.g. the adaptive exponential integrate-and-fire model) with parameter sets for 4 prominent neuron types in the FACETS data base. | 30 D4-3 |
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| Report on the generic rules for constructing compartmental dendritic models which have active ion-channels on the soma, dendrite, and axon. | 24 D4-1 |
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| Report on conductance based point neurons (with minimal number of ion channels) with parameter sets for 4 prominent neuron types in the FACETS data base. (updated Feb09) | 24 D4-2 |
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| Progress report on model developments and comparison to experimental data This deliverable reports on network model development under FACETS during the first 18 months. In the first part, we review the development of 'generic' network models of active cortical states in cerebral cortex based on in vivo data. The properties of 'active states' (such as in waking animals) are reviewed, both for cellular and network properties. Different types of models are then overviewed, and these models are compared to experimental data. In the second part, we overview the present status of the development of models of the primary visual system pathway (retina, thalamus, primary visual cortex or V1). Different models are outlined, as well as some of their features. | 18 D16 |
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| Report on hardware simulations of single-cell and network benchmarks The whole hardware system developed in WP6 is used for network simulations. In this deliverable, we study the activity of excitatory neurons network with all-to-all connectivity and STDP algorithm, and the influence of synaptic noise inputs with different rates and different correlations. We present some results among more than one hundred simulations. | 18 D17 |
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| Report on the definition of dataformat for the WP3 results in the repository For eficient sharing of data between experimental groups and between experimentalists and modellers within the FACETS project, a common data format is required for each data-type, together with tools for converting data between the common formats and the local formats used by individual groups. This report concerns the common formats for the data obtained in Workpackage 3. There is some overlap with Workpackage 2 (electrophysiological recordings of membrane potentials and membrane currents), and here the same common format will be used in both workpackages. | 12 D11 |
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| Identification of standardized compartemental toplogy for each cell type Biologically realistic single neuron modeling mainly depends on morphology, passive properties, ion channel combination, density and distribution. Here we report a modeling effort to replicate different electrical classes in detailed models of reconstructed excitatory and inhibitory neurons from the rat somato-sensory cortex. The robustness of these models is examined under different conditions including varying current stimulation, and changing morphology. We have observed that in most cases dendritic morphology does not strongly affect the electrical behavior | 12 D13 |
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| Equivalence report with the first generations of FACETS hardware from WP6 and WP7 We describe here the tests realized so far concerning the equivalence between specific integrated circuits to emulate neurons, and numerical simulations of the same models as those implemented on circuits. The two types of hardware developed in FACETS are described successively. First, we overview the analog circuits (ASICs) developed by the ENSEIRB partner, along with numerical simulations of the corresponding models. In the second part, we describe the circuits developed by the UHEI partner and their numerical counterpart. It is important to note that both types of hardware are presently at the stage of prototype - a second generation is in progress and a second report about the equivalence between analog and numeric simulations is planned for the second project period (month 30). | 12 D15 |
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| Hardware specifications for the next generation of neural ASIC circuits The mixed analog-digital neural simulator developed in WP6 is based on ASICs that are controlled by a software /hardware custom environment. These ASICs are the analog core of the simulator, and compute the activity of artificial neurons, modeled using a conductancebased representation (Hodgkin-Huxley formalism). The ASICs are full custom analog circuits designed in a sub-micron SiGe technology. This report presents the first generation of circuits designed and fabricated during the first year of the project and identifies the key points that will be addressed in the next generation to improve their functionality. | 12 D18 |
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| Concept of a common data model for neuroscience simulations One FACETS goal is to build a common data model for describing neuroscience simulation models. The FACETS project provides an ideal infrastructure for this important task by:
After this first 12 months of the project the consortium has provided and evaluated a declarative (FacetsML) and a procedural (PyNN) description of neurons and networks within the scope of this project. Both speci cations are available as cooperative open-source document bundles, FacetsML being in a software forge and PyNN being in an internal FACETS repository. As a step further, a prototype WYSIWYG editor for FacetsML has been developed, and requires evaluation not only by computer scientists but also by other colleagues. All specifications are computer language independent, written in XML (XSD schema and XSL transformation) and based on W3C standards. Utility tools are developed in Java for maximal portability. Technical tools are developed in Python which is the language used by most existing simulators within the consortium. Integration between Java and Python components is straightforward, using existing tools. | 12 D23 |
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| Project Presentation A presentation of the FACETS project. | 6 D 2 |
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| Report on comparison of existing software structures and data models in single neuron databasing This report surveys various single cell databases and their underlying principles and from there derives a database architecture for storing single cell data for the FACETS project. Experimental data on neurons is comprised of manifold information such as electrophysiological response to stimulus, the morphology of the cell, the gene expression data of ion channels, immunohistochemical data on ion channel distributions. Furthermore, it is important to conserve information about the environment of the neuron, ie. the microcircuit they are part of. It is thus desirable to store in addition properties of the microcircuit such as locations and types of synapses, voltage clamp data revealing the ion currents in neurons and pharmacological data on the different pre and postsynaptic receptors used in a microcircuit, numbers and frequencies of occurrence of specific neurons etc. Yet, the context of the FACETS project requires not only the integration the data of several experimental labs but also the availability of this data for the modeling workflow as well as the organization of the individual cell?s model data itself and potentially model data of microcircuits. Thus it is imperative for a single cell database to be able to store this multidimensional data and provide means for integrating and correlating the data at various levels of details. | 6 D12 |
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