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Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuroscience has to become "big science" - we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. Such "data ladders" will help us to meet the third challenge - the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure–function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). Significance There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.tins.2005.08.004 Byline: Gilad Silberberg (a)(b), Sten Grillner (a), Fiona E.N. LeBeau (c), Reinoud Maex (d), Henry Markram (b) Abstract: The functions performed by different neural microcircuits depend on the anatomical and physiological properties of the various synaptic pathways connecting neurons. Neural microcircuits across various species and brain regions are similar in terms of their repertoire of neurotransmitters, their synaptic kinetics, their short-term and long-term plasticity, and the target-specificity of their synaptic connections. However, microcircuits can be fundamentally different in terms of the precise recurrent design used to achieve a specific functionality. In this review, which is part of the TINS Microcircuits Special Feature, we compare the connectivity designs in spinal, hippocampal, neocortical and cerebellar microcircuits, and discuss the different computational challenges that each microcircuit faces. Author Affiliation: (a) Nobel Institute for Neurophysiology, Department of Neuroscience, Karolinska Institutet, S-17177 Stockholm, Sweden (b) Laboratory for Neural Microcircuitry, Brain Mind Institute, EPFL, Lausanne CH-1015, Switzerland (c) School of Neurology, Neurobiology and Psychiatry, University of Newcastle-upon-Tyne, Newcastle-upon-Tyne, NE2 4HH, UK (d) Laboratory of Theoretical Neurobiology, Institute Born-Bunge, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain. [ABSTRACT FROM AUTHOR]

Motivation: From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled. Results: We present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments. [ABSTRACT FROM AUTHOR]

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases. [ABSTRACT FROM AUTHOR]

Abstract: The Human Brain Project (HBP) is a candidate project in the European Union''s FET Flagship Program, funded by the ICT Program in the Seventh Framework Program. The project will develop a new integrated strategy for understanding the human brain and a novel research platform that will integrate all the data and knowledge we can acquire about the structure and function of the brain and use it to build unifying models that can be validated by simulations running on supercomputers. The project will drive the development of supercomputing for the life sciences, generate new neuroscientific data as a benchmark for modeling, develop radically new tools for informatics, modeling and simulation, and build virtual laboratories for collaborative basic and clinical studies, drug simulation and virtual prototyping of neuroprosthetic, neuromorphic, and robotic devices. [Copyright &y& Elsevier]

To understand the interface between global brain function and molecular neuroscience – that is, the microcircuit level – a major challenge. Such understanding is prerequisite if we are to account for neural function in cellular terms. Very few vertebrate microcircuits are yet understood because their analysis is demanding technically. In this review of the TINS Microcircuits Special Feature, we attempt to shed light on the problem by comparing the operation of four types of microcircuit, to identify common molecular and cellular components. Central pattern generator (CPG) networks underlying rhythmic movements and hippocampal microcircuits that generate gamma and theta rhythms are compared with the neocortical microcircuits used in cognitive tasks and a cerebellar network. The long-term goal is to identify the components of a molecular and synaptic tool kit for the design of different microcircuits. [Copyright &y& Elsevier]

Structural and functional diversity of GABAergic interneurons has become increasingly central in our understanding of the elemental steps of information processing in the brain. The use of different molecular, electrophysiological and anatomical techniques has provided a wealth of new information regarding GABAergic interneurons over the past decade but it has also led to confusion regarding the number of subtypes of GABAergic interneurons. Combinatorial approaches that also consider multiple parameters seem now to offer renewed hope for finally clarifying the structural diversity of GABAergic interneurons. New molecular techniques have become a powerful tool for exposing the functional diversity of GABAergic neurons at the cellular, microcircuit and systems levels. This article reviews literature regarding molecular tools that have been used, or that appear promising for future attempts, to classify GABAergic interneurons. Some important limitations will also be indicated. [Copyright &y& Elsevier]

The article discusses the technological advances in neuroscience. Highlighting the efforts of Allen Institute for Brain Science to map the transcriptome of complete mouse brain the article says that industrial revolution has reached the neuroscience. The article says that through the use of technology the institute created a three dimensional Allen Brain Atlas (ABA) that provides a view of all the areas of mouse brain. It is concluded that this begins an era in neuroscience.

An abstract is presented of the research paper "The modular cross-synaptic nature of LTP/LTD following on-going neural activity," by Alex Loebel and colleagues, which was presented at the Twentieth Annual Computational Neuroscience Meeting held in Sweden in July 2011.

An abstract of the article "The Blue Brain Project: Calibrating the Neocortical Column," discussed at the Sixteenth Annual Computational Neuroscience Meeting, held in Toronto, Canada is presented.

An abstract of the article "Systematic Mapping of Neural Function to Morphology," discussed at the Sixteenth Annual Computational Neuroscience Meeting, held in Toronto, Canada is presented.

A major challenge to understanding behavior is how the nervous system allows the learning of behavioral sequences that can occur over arbitrary timescales, ranging from milliseconds up to seconds, using a fixed millisecond learning rule. This article describes some potential solutions, and then focuses on a study by Mehta et al. that could contribute towards solving this puzzle. They have discovered that an experience-dependent asymmetric shape of hippocampal receptive fields combined with oscillatory inhibition can serve to map behavioral sequences on a fixed timescale. [Copyright &y& Elsevier]