Using all potential -sustaining workability and productivity through age specifíc working

Much has been said and written about the facts and implications ofdemographic change over the recent years. The message seems to slowly land: Oursociety is ageing, and so is Europe’s workforce. Thanks to various European and national initiatives and programmes, “age management in the workplace” has become a much-used term amongst researchers and practitioners in the fields ofoccupational Sciences, workplace health-promotion and related areas. Nevertheless- due to prevailing early-retirement practices in many countries over the last decades - the concept of“age specifíc working” or even the notion ofhaving to deal with an older workforce in the nearer future is still pretty new to those who will be most affected by the current developments: Europe’s companies and organisations, especially the many smaller ones among the

Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

The diversity of modeling approaches in computational neuroscience makes model sharing, retrieval, reuse and reproducibility difficult and even sometimes impossible. To address this problem, standardized languages have been developed by and for the community, such as NeuroML[1], PyNN [2] and NineML (http://software.incf.org/software/nineml). Although these languages enable software interoperability and therefore model reuse and reproducibility, they lack semantic information that would facilitate efficient model sharing and retrieval. In the context of the INCF Multi-Scale Modeling (MSM) program, we have developed an ontology to annotate spiking network models described with NineML and other structured model description languages. Ontologies are formal models of knowledge in a particular domain and composed of classes that represent concepts defining the field as well as the logical relations that link these concepts together [3]. These classes and relations have unique identifiers and definitions that allow unambiguous annotation of digital resources such as web pages or model source code. Implemented in a machine-readable format, these knowledge models can be used to design more efficient and intuitive information retrieval systems for experts in the field. We are proposing the first version of the Computational Neuroscience Ontology or CNO. This ontology is composed of 207 classes representing general concepts related to computational neuroscience organized in a hierarchy of concepts. CNO is currently available on Bioportal (http://bioportal.bioontology.org/ontologies/3003). The design of CNO follows some of the recommendations of the Open Biological and Biomedical Ontologies (OBO) community and is compatible with the ontologies developed and maintained within the Neuroscience Information Framework (NIF, [4]http://www.neuinfo.org). Integration with this large federation of neuroscience ontologies has two main advantages: (1) it allows the linking of models with biological information, creating a bridge between computational and experimental knowledge bases; (2) as ontology development is an iterative process that relies on inputs from the community, NIF has developed NeuroLex (http://neurolex.org), an effective collaborative platform, available for community inputs on the content in CNO. With the further development of CNO based on inputs from the community, we hope CNO will provide a useful framework to federate digital resources in the field of computational neuroscience

Correlations in spiking neuronal networks with distance dependent connections

Can the topology of a recurrent spiking network be inferred from observed activity dynamics? Which statistical parameters of network connectivity can be extracted from firing rates, correlations and related measurable quantities? To approach these questions, we analyze distance dependent correlations of the activity in small-world networks of neurons with current-based synapses derived from a simple ring topology. We find that in particular the distribution of correlation coefficients of subthreshold activity can tell apart random networks from networks with distance dependent connectivity. Such distributions can be estimated by sampling from random pairs. We also demonstrate the crucial role of the weight distribution, most notably the compliance with Dales principle, for the activity dynamics in recurrent networks of different types

NineML: the network interchange for neuroscience modeling language

The growing number of large-scale neuronal network models has created a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To foster community efforts towards such standards, the International Neuroinformatics Coordinating Facility (INCF) has formed its Multiscale Modeling program, and has assembled a task force of simulator developers to propose a declarative computer language for descriptions of large-scale neuronal networks. The name of the proposed language is "Network Interchange for Neuroscience Modeling Language" (NineML) and its initial focus is restricted to point neuron models. The INCF Multiscale Modeling task force has identified the key concepts of network modeling to be 1) spiking neurons 2) synapses 3) populations of neurons and 4) connectivity patterns across populations of neurons. Accordingly, the definition of NineML includes a set of mathematical abstractions to represent these concepts. NineML aims to provide tool support for explicit declarative definition of spiking neuronal network models both conceptually and mathematically in a simulator independent manner. In addition, NineML is designed to be self-consistent and highly flexible, allowing addition of new models and mathematical descriptions without modification of the previous structure and organization of the language. To achieve these goals, the language is being iteratively designed using several representative models with various levels of complexity as test cases. The design of NineML is divided in two semantic layers: the Abstraction Layer, which consists of core mathematical concepts necessary to express neuronal and synaptic dynamics and network connectivity patterns, and the User Layer, which provides constructs to specify the instantiation of a network model in terms that are familiar to computational neuroscience modelers. As part of the Abstraction Layer, NineML includes a flexible block diagram notation for describing spiking dynamics. The notation represents continuous and discrete variables, their evolution according to a set of rules such as a system of ordinary differential equations, and the conditions that induce a regime change, such as the transition from subthreshold mode to spiking and refractory modes. The User Layer provides syntax for specifying the structure of the elements of a spiking neuronal network. This includes parameters for each of the individual elements (cells, synapses, inputs) and the grouping of these entities into networks. In addition, the user layer defines the syntax for supplying parameter values to abstract connectivity patterns. The NineML specification is defined as an implementation-neutral object model representing all the concepts in the User and Abstraction Layers. Libraries for creating, manipulating, querying and serializing the NineML object model to a standard XML representation will be delivered for a variety of languages. The first priority of the task force is to deliver a publicly available Python implementation to support the wide range of simulators which provide a Python user interface (NEURON, NEST, Brian, MOOSE, GENESIS-3, PCSIM, PyNN, etc.). These libraries will allow simulator developers to quickly add support for NineML, and will thus catalyze the emergence of a broad software ecosystem supporting model definition interoperability around NineML

How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime

Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics, specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningful properties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily. When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supracritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- or fluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability. In particular, if neurons are mean-driven, the linearization has a very simple form and becomes independent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance are important parameters in the determination of the critical weight. We demonstrate that interestingly even in “intermediate” regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical coupling strength. We moreover analyze the effects of structural randomness by rewiring individual synapses or redistributing weights, as well as coarse-graining on the formation of inhomogeneous activity patterns

Robust parallel decision-making in neural circuits with nonlinear inhibition.

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes [Formula: see text] time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick's law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick's law may be a symptom of near-optimal parallel decision-making with noisy input