Reconstructing the brain: from image stacks to neuron synthesis

Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high quality reconstructed neurons. We propose a novel extension of this workflow to use this data stream to generate an unlimited number of statistically equivalent, yet distinct, digital morphologies. This will bring automated processing of reconstructed cells into digital neurons to the wider neuroscience community, and enable a range of morphologically- accurate computational models.

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON

Michael Lee Hines, Felix Schürmann

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

Mit Press2016

Data-driven reconstruction of a point neuron mouse brain

Csaba Erö

In this work, we present a semi-automatic method to reconstruct a mouse whole-brain model at the point-neuron level by integrating a wide array of biological data. Our process has three parts: cell position and type assignment, connectivity mapping, and simulation. Additional validation is performed at every step of the workflow. We obtain cell positions from a voxelized data sets derived from high-resolution Nissl stained microscope image stacks in the Allen Mouse Brain reference atlas (Lein, et al. 2007), and global constraint numbers for the whole-brain (Herculano-Houzel, Ribeiro, et al. 2011). We then assign the type of each cell using In Situ Hybridization (ISH) image data from the same atlas. Cells are classified as glia subtypes, excitatory neurons, or inhibitory neurons, leaving the possibility to further expand the diversity of assigned cell types by using more genes in this step. We furthermore integrate region-specific cell densities from literature into our model. We then study cell type correlations throughout the brain and compare the resulting numbers to literature data in order to validate the process. In the second step, we use two-photon tomography images of recombinant Adeno-Associated Virus (rAAV) labeled axonal projections from the Allen Mouse Connectivity Atlas (Oh, et al. 2014) to determine the mesoscale connectivity between the neurons in different brain regions. For this step a comprehensive comparison to experimental data is difficult due to lack of similar connectivity data. We then obtain a network configuration that can be simulated with state-of the art software like Nest. We show results from a simulated whisker stimulation experiment and compare the evoked activity patterns to imaging data from Voltage Sensitive Dye (VSD) experiments (Ferezou, Haiss, et al. 2007). Finally, we build a glossary of possible whole-brain behaviors of our model, and briefly explore the possibility of a biologically plausible mapping of its input and output pathways.

EPFL2018

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