Neuroscience thinks big (and collaboratively)

Despite cash-strapped times for research, several ambitious collaborative neuroscience projects have attracted large amounts of funding and media attention. In Europe, the Human Brain Project aims to develop a large-scale computer simulation of the brain, whereas in the United States, the Brain Activity Map is working towards establishing a functional connectome of the entire brain, and the Allen Institute for Brain Science has embarked upon a 10-year project to understand the mouse visual cortex (the MindScope project). US President Barack Obama's announcement of the BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies Initiative) in April 2013 highlights the political commitment to neuroscience and is expected to further foster interdisciplinary collaborations, accelerate the development of new technologies and thus fuel much needed medical advances. In this Viewpoint article, five prominent neuroscientists explain the aims of the projects and how they are addressing some of the questions (and criticisms) that have arisen.

Hybrid Representation and Visualization Methods Applied to Neuronal Microcircuits

Sébastien Lasserre

For the past twenty years, scientific visualization has assisted simulation-based research to explore problems of organized complexity. Environmental research, social sciences, astronomy, and modern physics have exploited the increasing power of computer graphics to interactively visualize three-dimensional models. Of late, neuroscience has witnessed the use of large-scale simulation with biologically detailed neuron models, and aspires to benefit from scientific visualization too. This thesis is the result of a close collaboration with the Blue Brain Project, a neuroscience endeavor working on biologically detailed large-scale simulation of the brain, and tries to answer the following question: How can scientific visualization improve neuroscience insights in the context of simulation-based research? Scientific visualization of a model typically preserves its spatial layout. In continuity with this axiom, we identified in more depth the requirements for a scientific visualization solution in neuroscience. Thus, the preservation of the domain specificity, the hybrid visualization, the validation, and the adaptation to the limitations of the human-visual perception enhance the readability of the generated results. We applied these requirements to elaborate an hybrid representation of neuronal structures, and proposed a unified framework of visualization methods which preserves the domain specificities of neuroscience. This framework was finally assessed in case studies, where neuroscientific investigations about structure, composition, and dynamics of neuronal microcircuit challenge the developed visualization solutions to derive insight. We believe that these representations and visualization methods form a solid ground for the elaboration of virtual instruments dedicated to a new approach of neuroscience, where in silico landscapes of the brain are complementary to the classical in vitro and in vivo investigations.

EPFL2010

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008

Predictive Engineering the Membrane Composition of Neocortical Neurons

Georges Khazen

The brain is the most mysterious and intricate biological structure known to man. It dominates the way we live, think, reason and behave. It is built of billions of neurons that communicate with each other through trillions of connections using electrical signals. The neocortex is undoubtedly the most complex brain region, hosting all higher brain functions such as sensory perception and cognitive behavior. Scientists have been struggling for more than a century to identify the anatomical blueprint of its cellular organization and to understand its dynamic behavior. The Blue Brain Project aims to develop the first simulation-based research environment for modeling and studying the basic building blocks of the neocortex, the neocortical column. The model column is based on data obtained from the juvenile rat somatosensory cortex. The best way to build such a large-scale model is by following a data-constraint driven approach and integrating biological information at different levels. In this thesis, I present novel informatics approaches to systematically constrain the membrane protein composition of different neocortical neuron types. In the first study, I investigate the single-cell gene expression patterns of ion channels in different neuron types and present a combinatorial rule extractor model that reverse engineers the observed expression rules in ten different neocortical neuron types. In the second study, I present a physical constrain checker that mines different publicly available data resources regarding all known membrane proteins such as channels and receptors, and extracts available information relating to their specific location on different parts of the neuronal morphologies, their 3D morphological structures as well as their genetic and proteomic annotations. Combining the results from both studies allows us to build neuronal models that better reflect the biological reality of neocortical neurons, ensuring the correct localization and distribution of membrane proteins across neuronal morphologies and computing their maximum count number based on their molecular surface areas and the available neuronal membrane surface area. This novel data-constraint driven approach is the first step towards integrating existing biological knowledge and constraints from various data sources into the process of building neuron models, in this way improving the neuron models as they come closer to the biological reality of real neurons.

EPFL2011