Blue Brain’s Scientific Milestones

The establishment of simulation neuroscience as a vital complement to experimental, theoretical, and clinical approaches was underpinned by the achievement of essential scientific milestones.

14 Key Milestones Enabling Simulation Neuroscience

Mining and populating neuroscience data and knowledge in knowledge graphs

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science. Sy, M. F., et al. (2022). Semantic Web, 1–31.
The Neuron Phenotype Ontology: A FAIR approach to proposing and classifying neuronal types. Gillespie, T. H., et al. (2022). Neuroinformatics.
Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. Eriksson, O., et al. (2022). eLife, 11, e69013.
A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience. Shardlow, M., et al. (2018). Neuroinformatics. 15 Nov 2018, 1-6.
A Framework for Collaborative Curation of Neuroscientific Literature. O’Reilly, C., et al. (2017). Front Neuroinform. 11, 27.

Establishing the number and spatial distributions of all the neurons and glia in each of the mouse’s brain regions

Reconstruction and Simulation of Neocortical Microcircuitry. Markram, H., et al. (2015). Cell, 163, 456-492.
Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region. Romani, A., et al. (2024). PLoS Biology, 22, e3002861.
A derived positional mapping of inhibitory subtypes in the somatosensory cortex. Keller, D., et al. (2019). Front. Neuroanat. 13, 78.
Generating brain-wide connectome using synthetic axonal morphologies. Petkantchin, R., et al. (2024). bioRxiv.
Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situRNA hybridization. Keller, D., et al. (2023). Frontiers in Neuroanatomy, 17.
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy. Reimann, M. W., et al. (2024). eLife.

Deriving all known cell-types in each brain region – according to morphological, electrical and genetic criteria

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex. Wei, Y., et al. (2023). Nature Communications, 14(1), 2344.
Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons. Roussel, Y., et al. (2023). PLOS Computational Biology, 19(1), e1010058.
Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Nandi, A., et al. (2022). Cell Reports, 40(6), 111176.

Creating and enhancing a 3D digital atlas of all known neurons, glial cells and microvasculature in the whole mouse brain

Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations. Bolaños-Puchet, S., et al. (2024). Imaging Neuroscience, 2, 1–20.
A whole-body musculoskeletalmodel of the mouse. Tata Ramalingasetty, S., et al. (2021). IEEE Access, 9, 163861–163881.
A method to estimate the cellular composition of the mouse brain from heterogeneous datasets. Rodarie, D., et al. (2022). PLOS Computational Biology, 18(12), e1010739.
A Cell Atlas for the Mouse Brain. Erö, C., et al. (2018). Frontiers in Neuroinformatics. 12, 84.

Mathematically growing the dendrites of all neurons found in the mouse brain

Of mice and men: Increased dendritic complexity gives rise to unique human networks. Kanari, L., et al. (2023). bioRxiv.
From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem. Curry, J., et al. (2023). Computational Geometry, version of record: 18 July 2023, Vol. 116, 103031.
Computational synthesis of cortical dendritic morphologies. Kanari, L., et al. (2022). Cell Reports, 39(1), 110586.
Objective Morphological Classification of Neocortical Pyramidal Cells. Kanari, L., et al. Cerebral Cortex, Volume 29, Issue 4, April 2019, Pages 1719–1735.
A Topological Representation of Branching Neuronal Morphologies. Kanari, L., et al. (2018a). Neuroinformatics. 16, 3–13.
Framework for efficient synthesis of spatially embedded morphologies. Vanherpe, L., et al. (2016). Phys Rev. E 94, 023315.

Mathematically growing the local, regional and whole brain axonal projections of all neurons found in the mouse brain

Generating brain-wide connectome using synthetic axonal morphologies. Petkantchin, R., et al. (2024). bioRxiv.
A Topological Representation of Branching Neuronal Morphologies. Kanari, L., et al. (2018a). Neuroinformatics. 16, 3–13.
Framework for efficient synthesis of spatially embedded morphologies. Vanherpe, L., et al. (2016). Phys Rev. E 94, 023315.

Data-driven electrical modeling to recreate all types of electrical behaviors found in the mouse brain

Reconstruction and Simulation of Neocortical Microcircuitry. Markram, H., et al. (2015). Cell, 163, 456-492.
A multimodal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays. Buccino, A. et al. (2024). Neural Computation, 1–46.
A universal workflow for creation, validation and generalization of detailed neuronal models. Reva, M., et al. (2023). Patterns (Cell Press), 100855.
Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub. Bologna, L. L., et al. (2023). Frontiers in Neuroinformatics, 17, 1271059.
Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. Arnaudon, A., et al. (2023). iScience (Cell Press), 108222.
Data‐driven integration of hippocampal CA1 synaptic physiology in silico. Ecker, A., et al. (2020). Hippocampus, Wiley. 30(11), 1129–1145.
Data driven building of realistic neuron model using IBEA and CMA evolution strategies. Damart, T., et al. (2020). GECCO ’20 Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 35–36.
Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. Iavarone E., et al. (2019) PLOS Computational Biology. 15(5): 1-23. e1006753.
Estimating the readily-releasable vesicle pool size at layer 5 pyramidal connections in the neocortex. Barros-Zulaica, N., et al. Front. Synaptic Neurosci., 15 October 2019.
The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. Migliore, R., et al. (2018). PLOS Computational Biology. 14(9).

Algorithmically recreating the connectome – region to region, area to area and neuron to neuron levels

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region. Romani, A., et al. (2024). PLoS Biology, 22, e3002861.
Reconstruction and Simulation of Neocortical Microcircuitry. Markram, H., et al. (2015). Cell, 163, 456-492.
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation. Isbister, J. B., et al. (2024). eLife.
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy. Reimann, M. W., et al. (2024). eLife.
A null model of the mouse whole-neocortex micro-connectome. Reimann, M.W., et al. Nature Communications 29 August 2019.
Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Reimann, M.W., et al. (2017). Cereb.Cortex 27, 4570–4585.
An Algorithm to Predict the Connectome of Neural Microcircuits. Reimann, M., et al. (2015). 2015. Frontiers in Neural Circuits 9 28.

Validating the biological fidelity of digital brain tissue

Reconstruction and Simulation of Neocortical Microcircuitry. Markram, H., et al. (2015). Cell, 163, 456-492.
Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region. Romani, A., et al. (2024). PLoS Biology, 22, e3002861.
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy. Reimann, M. W., et al. (2024). eLife.

Mimicking biological experiments and making verifiable predictions with digital brain tissue

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations. Pokorny, C., et al. (2024). Network Neuroscience, 1–51.
Cortical cell assemblies and their underlying connectivity: An in silico study. Ecker, A., et al.(2024). PLOS Computational Biology, 20(3), e1011891.
Reconstruction and Simulation of Neocortical Microcircuitry. Markram, H., et al. (2015). Cell, 163, 456-492.
Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs. Vitale, P., et al. (2023). Frontiers in Cellular Neuroscience, 17, 1281932.
Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region. Romani, A., et al. (2024). PLoS Biology, 22, e3002861.
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation. Isbister, J. B., et al. (2024). eLife.
Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep. Iavarone, E., et al. (2023). Cell Reports, 42(3), 112200.
In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations. Newton, T. H., et al. (2021). Nature Communications, 12(1), 3630.
Dense computer replica of cortical microcircuits unravels cellular underpinnings of auditory surprise response. Amsalem, O., et al. (2020). bioRxiv, 2020.05.31.
Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex. Colangelo, C., et al. (2019). Frontiers in Neural Circuits. 13, 24.
Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity. Ramaswamy, S., et al. (2018). Front. Neural Circuits. 12, 77–77.
Distinct Activity Profiles of Somatostatin-Expressing Interneurons in the Neocortex. Ramaswamy, S., et al. (2017). Front Cell Neurosci. 11, 273.
Reconstruction and visualization of large-scale volumetric models of neocorticalcircuits for physically-plausible in silico optical studies. Abdellah, M., et al. (2017). BMC Bioinformatics. 18, 402.
From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells. Amsalem, O., et al. (2016). Cereb. Cortex. 26, 3655–3668.

Demonstrating the utility of biophysically detailed modeling for studying the structure-function relation

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations. Pokorny, C., et al. (2024). Network Neuroscience, 1–51.
Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes. Santander, D. E., et al. (2024). bioRxiv.
Cortical cell assemblies and their underlying connectivity: An in silico study. Ecker, A., et al. (2024). PLOS Computational Biology, 20(3), e1011891.
Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies. Reimann, M. W., et al. (2022). PLOS ONE, 17(1), e0261702.
The role of hub neurons in modulating cortical dynamics. Gal, E., et al. (2021). Frontiers in Neural Circuits, 15, 96.
Impact of higher-order network structure on emergent cortical activity. Nolte, M., et al. (2020). Network Neuroscience. 4(1), 292–314.
Neuron geometry underlies universal network features in cortical microcircuits. Gal, E., et al. (2020). bioRxiv, 2020.05.07.
Dense computer replica of cortical microcircuits unravels cellular underpinnings of auditory surprise response. Amsalem, O., et al. (2020). bioRxiv, 2020.05.31.
Cortical reliability amid noise and chaos. Nolte M., et al. Nature Communications, 22 August 2019
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Reimann, M.W., et al. (2017). Front Comput Neurosci. 11, 48.

Reconstructing the neuro-glial-vascular system with submicron precision and molecular simulation of the metabolic system of the brain

Structural and molecular characterization of astrocyte and vasculature connectivity in the mouse hippocampus and cortex. Lorin, C., et al. (2024). Glia, 1–21.
Breakdown and repair of the aging brain metabolic system. Shichkova, P., et al. (2023). bioRxiv.
A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Colombo, G., et al. (2022). Nature Neuroscience, 25(10), 1379–1393.
Representing stimulus information in an energy metabolism pathway. Coggan, J. S., et al. (2022). Journal of Theoretical Biology, 540, 111090.
Digital reconstruction of the neuro-glia-vascular architecture. Zisis, E., et al. (2021). Cerebral Cortex, 31(12), 5686–5703.
A standardized brain molecular atlas: A resource for systems modeling and simulation. Shichkova, P., et al. (2021). Frontiers in Molecular Neuroscience, 14, 251.
Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling. Coggan, J.S., et al. (2020). Journal of Theoretical Biology, 487, 110123.
Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate. Coggan, J.S., et al. (2018b). PLOS Computational Biology 14 (8).
A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble. Coggan, J.S., et al. (2018a). Frontiers in Neuroscience 12, 664.

Unifying algorithms for synaptic plasticity to study learning and adaptation to stimuli at all scales

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome. Ecker, A., et al. (2024). eLife,
A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex. Chindemi, G., et al. (2022). Nature Communications, 13(1), 3038.

Improving the performance of brain simulation software and model representation to enable robust, scientifically-valuable & large-scale simulation campaigns

MOD2IR: High-performance code generation for a biophysically detailed neuronal simulation DSL. Mitenkov, G., et al. (2023). Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction, 203–215.
Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute. Aimone JB, et al. (2023) Front. Neuroinform. Volume 17.
Statistical emulation of neural simulators: Application to neocortical L2/3 large basket cells. Shapira, G., et al. (2022). Frontiers in Big Data, 5.
Computational concepts for reconstructing and simulating brain tissue. Schürmann, F., et al. (2022). Chapter 10. In: Giugliano, Negrello, Linaro (eds.) Computational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks. Series: Advances in Experimental Medicine and Biology (vol. 1359, pp. 237–259). Springer International Publishing.
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale. Chen, W., et al. (2022). Frontiers in Neuroinformatics, 16, 883742.
Modernizing the NEURON simulator for sustainability, portability, and performance. Awile, O., et al. (2022). Research topic: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute). Frontiers in Neuroinformatics, 16.
HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. Sáray, S., et al. (2021). PLOS Computational Biology, 17(1), e1008114.
Efficient distributed transposition of large-scale multigraphs and high-cardinality sparse matrices. Magalhaes, B., et al. (2020). arXiv, 10 December 2020. http://arxiv.org/abs/2012.0601
Fully-asynchronous fully-implicit variable-order variable-timestep simulation of neural networks. Magalhães, B., et al. (2020). In Krzhizhanovskaya, V. et al. (Eds.),ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12141. Springer, Cham.
An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language. Kumbhar, P., et al. (2020). In Krzhizhanovskaya, V. et al. (Eds.), ICCS 2020 Amsterdam: Lecture Notes in Computer Science, vol 12137. Springer, Cham.
The SONATA data format for efficient description of large-scale network models. Dai, K., et al. (2020). PLOS Computational Biology, 16(2), e1007696.
CoreNEURON: Performance and energy efficiency evaluation on Intel and Arm CPUs. Criado, J.,et al. (2020). 2020 IEEE International Conference on Cluster Computing (CLUSTER), 540–548.
Analytic performance modeling and analysis of detailed neuron simulations. Cremonesi, F., et al. April 3, 2020, The International Journal of High Performance Computing Applications. 34(4), 428–449. SAGE Publishing.
An efficient analytical reduction of detailed nonlinear neuron models. Amsalem, O., et al. (2020). Nature Communications, 11(1), 288.
Exploiting flow graph of system of odes to accelerate the simulation of biologically-detailed neural networks. Magalhães, B.R.C., et al. (2019). In the proceedings of IEEE 2019 International Parallel and Distributed Processing Symposium (IPDPS), (Rio de Janeiro, Brazil), 176–187.
Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. Magalhães B.R.C., et al.(2019) In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11538. Springer International Publishing, 421–434.
CoreNEURON : An optimized compute engine for the neuron simulator. Kumbhar, P., et al. (2019). Front. Neuroinform. 13, 63.
CoreNEURON : An optimized compute engine for the neuron simulator. Kumbhar, P., et al. (2019). Front. Neuroinform. 13, 63.
Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers. Schumann T, et al. (2017). In High-Performance Scientific Computing: First JARA-HPC Symposium, JHPCS 2016, October 4–5, 2016, Revised Selected Papers, Di Napoli, E. et al., Eds (Aachen, Germany: Springer International Publishing), pp. 160–169.
BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience. Van Geit, W., et al.(2016) Front Neuroinform 10.

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, a part of the rodent brain and initial draft of a part of the human brain.

The software, data, models, and algorithms are now openly accessible for the community to use, test, and enhance. As these models evolve, their predictions extend into areas beyond the reach of biological brains, unlocking unprecedented possibilities. They serve as powerful tools to explore hypotheses, refine biological experiments, and generate predictions for future investigations in principle at any level of brain organization.