Blue Brain’s Scientific Milestones

Blue Brain follows a rolling four-year roadmap with specific scientific milestones to achieve during this period as we work towards our ultimate goal of digitally reconstructing the entire mouse brain.

The scientific milestones, which are verified by independent scientists, guide the Blue Brain in our day-to-day science but when achieved and shared, help other brain initiatives and the wider scientific communities achieve their goals.

Milestone One

The automatic computer reconstruction of the electrical behavior of any neuron recorded in the brain.

Importance – this approach and respective software has become the standard for many who model neurons because it allows Blue Brain and others to automatically capture in the computer the realistic behavior of millions and even billions of neurons. This was the first essential step to building in the computer a whole brain of approximately a hundred billion neurons.

Achieved in – 2007

Publications

Druckmann, S., Banitt, Y., Gidon, A., Schürmann, F., Markram, H., Segev, I.,
A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data,
Frontiers in Neuroscience, Vol. 1, Issue 1, 2007.
DOI: 10.3389/neuro.01.1.1.001.2007

Druckmann, S., Berger, TK., Hill, S., Schürmann, F., Markram, H., Segev, I.,
Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data
Biol Cybern. 2008 Nov;99(4-5):371-9. Epub 2008 Nov 15.
DOI:
10.1007/s00422-008-0269-2

Druckmann, S., Berger, T., Schürmann, F., Hill, S,, Markram, H., Segev, I.,
Effective Stimuli for Constructing Reliable Neuron Models
PLoS Comp Biol, 7(8): e1002133
DOI:10.1371/ journal.pcbi.1002133

Hay, E., Hill, S., Schürmann, F., Markram, H., Segev, I.,
Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties
PLoS Comp Biol, 7(7): e1002107.
DOI: 10.1371/journal.pcbi.1002107

Hay, E, Schürmann, F., Markram, H., Segev, I.,
Preserving axosomatic spiking features despite diverse dendritic morphology
J Neurophysiol., 2013 Jun;109(12):2972-81.
DOI: 10.1152/jn.00048.2013. Epub 2013 Mar 27

Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, JD., Muller, EB., Schürmann, F., Segev, I., Markram, H.,
BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience
Front Neuroinform. 2016 Jun 7;10:17.
DOI: 10.3389/fninf.2016.00017. eCollection 2016.

Masoli, S., Rizza, MF., Sgritta, M., van Geit, W., Schürmann, F., D’Angelo, E.,
Single neuron optimization as a basis for accurate biophysical modelling: the case of cerebellar granule cells
Front. Cell. Neurosci. 2017; 11:71.
DOI: 10.3389/fncel.2017.00071

Migliore, R., Lupascu, CA., Bologna, LL., Romani, A., Courcol, J-D., Antonel, S., Van Geit, WAH., Thomson, AM., Mercer, A., Lange, S., Falck, J., Roessert, CA., Freund, TF., Kali, S., Muller, EB., Schürmann, F., Markram, H., Migliore, M.,
The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow
PLOS Computational Biology 14(9).
DOI: 10.1371/journal.pcbi.1006423 2018

Milestone Two

An algorithm that can recreate the connectome of a microcircuit of neurons. This algorithm allowed Blue Brain to capture the way that neurons are connected to each other in the brain and the 3D location of millions of synapses in the modelled microcircuit.

Importance – for the first time, Blue Brain and other scientists had a model to use to study how a neuron processes all the different types of synaptic inputs it receives and can begin understanding how the connectome shapes the function of a microcircuit.

Achieved in – 2015

Publications

Reimann, M.W., Muller, E., Ramaswamy, S., Markram, H: An Algorithm to Predict the Connectome of Neural Microcircuits. 2015. Frontiers in Neural Circuits 9 2015, 28.
DOI: 10.3389/fncom.2015.00120

Kozloski, J., Sfyrakis, K., Hill, S., Schürmann, F., Peck, C., Markram, H.,
Identifying, tabulating, and analyzing contacts between branched neuron morphologies
IBM Journal of Research and Development 2008, 52 (1.2), 43–55.
DOI: 10.1147/rd.521.0043

Hill, SL., Wang, Y., Riachi, I., Schürmann, F., Markram, H.,
Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits
PNAS, 2012 Oct 16;109(42):E2885-94.
DOI: 10.1073/pnas.1202128109. Epub 2012 Sep 18

Magalhaes, B., Tauheed, F., Heinis, T., Ailamaki, A., Schürmann, F., In: Kunkel, J., Balaji, P., Dongarra, J.,
An Efficient Parallel Load-balancing Framework for Orthogonal Decomposition of Geometrical Data
(eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science, vol 9697, Pp. 81-97. Springer, Cham
DOI: 10.1007/978-3-319-41321-1_5

Milestone Three

Reconstruction and Simulation of Neocortical Microcircuitry

To bring the different types of neurons and synapses together as a microcircuit, which was demonstrated in the form of the most biologically realistic copy to date of a neocortical column – the “CPU” of the neocortex.

Importance – it demonstrated the extent to which and the accuracy we can predict missing data, because it revealed the first glimpse of the cellular and synaptic map of the most complex microcircuit in the mammalian brain. It also provides a proof of concept, for building larger circuits such as whole brain regions. 

Achieved in – 2015

Publication

H. Markram et al, Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163, 2015, 456-492.

DOI: 10.1016/j.cell.2015.09.029

Model –  The Neocortical Microcircuit Collaboration Portal – https://bbp.epfl.ch/nmc-portal/welcome

Milestone Four

The validation and exploration of the emergent dynamics of the microcircuit in milestone three. The model gave rise to a whole range of states, solely by integrating measurements. It also provides new insights into the connectivity structure of cortical circuits and provided the key topological features of the neocortical microcircuit.

Importance – this was a key milestone because it showed, for the first time, that we could integrate all available data about a cortical microcircuit into a digital reconstruction that would give rise to a complex array of network states comparable to that observed in real circuitry. We could use this model to simulate the transition between states by changing ionic concentrations or conductances. As we can already compute the electrical field generated by all the elements in this circuit, this milestone paves the way for bridging between subcellular, cellular and synaptic activity and the electroencephalography (EEG) waveforms measured in the lab and clinic. 

Achieved in – 2015

Publications

Markram et al, Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163, 2015, 456-492.
DOI: 10.1016/j.cell.2015.09.029.    

Reimann, M., Anastassiou, C., Perin, R., Hill, S., Markram, H., Koch, C., (2013) A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79(2), 375-390, 2013.
DOI: 0.1016/j.neuron.2013.05.023

Gal, E., London, M., Globerson, A., Ramaswamy, S., Reimann, M.W., Muller, E., Markram, H., and Segev, I. (2017). Rich cell-type-specific network topology in neocortical microcircuitry. Nat. Neurosci. 20, 1004–1013.
DOI: 10.1038/nn.4576

Reimann, M., Horlemann, A., Ramaswamy, S., Muller, E., Markram, H. (2017). Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cereb.Cortex 27, 4570–4585.
DOI: 10.1093/cercor/bhx150

Reimann, M., Nolte, M., Scolamiero, M., Turner, K., Perin, R., Chindemi, G., Dłotko, P., Levi, R., Hess, K., Markram, H. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Front Comput Neurosci 11, 48.
DOI: 10.3389/fncom.2017.00048

Ramaswamy, S., Colangelo, C., Markram, H. (2018). Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity. Front. Neural Circuits 12, 77–77. 
DOI: 10.3389/fncir.2018.00077

Nolte, M., Reimann, M., King, J., Markram, H., Muller, E., Cortical reliability amid noise and chaos Nature Communications, 22 August 2019,
DOI: 10.1038/s41467-019-11633-8

Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., King, J., Kumbhar, P., Monney, C., Perin, R., Rössert, C., Van Geit, W., DeFelipe, J., Graupner, M., Segev, I., Markram, H., Muller, E. (2020). A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex. bioRxiv, 2020.04.19. 
DOI: 10.1101/2020.04.19.043117

Nolte, M., Gal, E., Markram, H., and Reimann, M.W. (2020). Impact of higher-order network structure on emergent cortical activity. Network Neuroscience. 4, 1–36.
DOI: 10.1162/netn_a_00124.

Amsalem, O., King, J., Reimann, M., Ramaswamy, S., Muller, E., Markram, H., Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response (June 2020)
DOI: 10.1101/2020.05.31.126466

Newton, T., Abdellah, M., Chevtchenko, G., Muller, E., Markram, H.,
Voltage-sensitive dye imaging reveals inhibitory modulation of ongoing cortical activity (2019)
DOI: 10.1101/812008

Milestone Five

Blue Brain solved a decade old problem of mathematically growing the shape of neurons (their morphology).

Importance – for Blue Brain, we needed to reach this milestone because normally one can only use neurons that are recorded in the brain in the laboratory and 3D digitally drawn under a microscope; this can never yield enough neurons to build whole brain model. This milestone is also important because it provides us with a new way, in the future, to build neurons by reading their genetic information. For example, in the future, we may be able to study the effect of genetic changes in the brain in terms of how they affect the shapes of neurons. 

Achieved in – 2019

Publications

Kanari, L., Ramaswamy, S., Shi, Y., Morand, S., Meystre, Julie., Perin, R., Abdellah, M., Wang, Y., Hess, K., Markram., Objective Morphological Classification of Neocortical Pyramidal Cells. Cerebral Cortex, Volume 29, Issue 4, April 2019
DOI: 10.1093/cercor/bhy339

Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K.,  Markram, H. (2018a). A Topological Representation of Branching Neuronal Morphologies. Neuroinformatics 16, 3–13.
DOI: 10.1007/s12021-017-9341-1

Vanherpe, L., Kanari, L., Atenekeng, G., Palacios, J., and Shillcock, J. (2016). Framework for efficient synthesis of spatially embedded morphologies. Phys Rev E 94, 023315.
DOI: 10.1103/PhysRevE.94.023315

Software release – the software used for the extraction of the persistence barcodes from neuronal reconstructions is available in the public domain https://github.com/BlueBrain/TMD under the GNU Lesser General Public License, version 3 (LGPLv3).

Milestone Six

Blue Brain validated that the methods developed to build microcircuits can be generalized to building a whole brain region with curved shape and differences in cellular composition and synaptic properties. Here we targeted the somatosensory cortex for which a paper is in preparation.

Importance – this demonstrates that the principles Blue Brain had developed to reconstruct the microcircuitry can be extended to a much larger region – around two million neurons.

Achieved in – 2020

Publication – pending 2020

Milestone Seven

An algorithm to connect the ~11 million neurons in the mouse neocortex.

This was a very challenging milestone because the data was very sparse. There existed some tracing data on which parts of the neocortex connect to which other parts, that we used to find a set of rules, which allowed us to predict the formation and location of 88 billion synapses that connect these neurons.

Importance – now Blue Brain can now build all the brain regions making up the neocortex. It also demonstrates how, completely surprising and novel solutions can be found using very sparse experimental data and it reveals a number of interesting principles of how every neuron is connected in the neocortex. Passing the milestone allows us now to build the first digital reconstruction of the entire neocortex.

Achieved in – 2019

Publication Reimann, M.W., Gevaert, M., Shi, Y., Lu, H., Markram, H., and Muller, E. A null model of the mouse whole-neocortex micro-connectome. Nature Communications 29 August 2019.
DOI: 10.1038/s41467-019-11630-x

Model releasedMouse whole-neocortex connectome model via the Blue Brain Portal

Milestone Eight

The eighth milestone was to extend our algorithmic reconstruction approach to structures with direct relevance for the neocortex. Specifically, we started with the thalamus due to its importance for funnelling sensory input to the neocortex.

Importance – this milestone was important because it allows us to refine insilico experiments of cortical models due to the improved details on the sensory input pathways and it furthermore allowed us to see how our algorithmic approaches generalize to other brain regions.

Achieved in – 2019

Publication on circuit – coming in 2020

Publication on neurons

Iavarone E., Yi J., Shi Y., Zandt B.J., O’Reilly C., Van Geit W., Rössert C., Markram, H., Hill, S.L. (2019) Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLOS Computational Biology 15(5): 1-23. e1006753.

DOI: 10.1371/journal.pcbi.1006753

Milestone Nine

The ninth milestone was to validate more generally that our algorithmic reconstruction approach works outside of the neocortex, which we tested by reconstructing the hippocampus in collaboration with external groups.

Importance – this milestone is important because it means that the processes and algorithms that we had developed for the neocortex also work, with some adaptation, for other brain regions. The specific adaptations taught us about different principles operating in their different brain regions.

Achieved in – 2019

Publication on circuit – coming in 2020

Publication on neurons

Migliore, R., Lupascu, C.A., Bologna, L.L., Romani, A., Courcol, J.-D., Antonel, S., Van Geit, W.A.H., Thomson, A.M., Mercer, A., Lange, S., Falck, J., Roessert, C. A., Freund, T. F., Kali, S., Muller, E. B., Schürmann, F., Markram, H., Migliore, M. (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology 14(9).

DOI: 10.1371/journal.pcbi.1006423

 

Milestone Ten

Our tenth milestone was to build a full cell atlas of every neuron and glial cell in the whole mouse brain. We published and released this atlas for the community to use in 2018.

ImportanceThis milestone is an important building block which will enable us to grow all the neuronal morphologies, capture all the different electrical behaviors of each type of neuron, and connect all the neurons to build the first draft of the whole mouse brain by 2024.

Achieved in – 2018

PublicationErö, C., Gewaltig, M.-O., Keller, D., and Markram, H. (2018). A Cell Atlas for the Mouse Brain. Frontiers in Neuroinformatics 12, 84.

DOI: 10.3389/fninf.2018.00084

Cell Atlashttps://portal.bluebrain.epfl.ch/resources/models/cell-atlas/

What is next?

So far, Blue Brain has established a solid approach to feasibly reconstruct, simulate, visualize and analyze a digital copy of mouse brain tissue and the whole mouse brain. We are now at the stage where we can accelerate the building of larger mouse brain regions and integrate more biological detail. Furthermore, we can use the modelled circuit for gaining new insights into the operation of large-scale circuits, connecting the micro-scale cellular level (e.g., membrane ion channels, synaptic and spike dynamics) to the macroscale level (the emergence of behavioural-related brain states) – e.g.,  the work by Reimann et al. (2013) or the recent works released as preprints by Amsalem et al., Newton et al. or Chindemi et al.