Abstract
In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.
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References
Yuste, R. et al. A community-based transcriptomics classification and nomenclature of neocortical cell types. Nat. Neurosci. 23, 1456–1468 (2020).
Ramón y Cajal, S. Histologie Du Système Nerveux de l’homme & Des Vertébrés (A. Maloine Editeurs, Paris, 1909).
Petilla Interneuron Nomenclature Group. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).
Gilbert, C. D. & Wiesel, T. N. Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature 280, 120–125 (1979).
Gouwens, N. W. et al. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat. Neurosci. 22, 1182–1195 (2019).
Jones, E. G. Varieties and distribution of non-pyramidal cells in the somatic sensory cortex of the squirrel monkey. J. Comp. Neurol. 160, 205–267 (1975).
Kanari, L. et al. Objective morphological classification of neocortical pyramidal cells. Cereb. Cortex 29, 1719–1735 (2019).
Martin, K. A. & Whitteridge, D. Form, function and intracortical projections of spiny neurones in the striate visual cortex of the cat. J. Physiol. 353, 463–504 (1984).
Peters, A. & Feldman, M. L. The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. I. General description. J. Neurocytol. 5, 63–84 (1976).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
de Vries, S. E. J. et al. A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nat. Neurosci. 23, 138–151 (2020).
Wang, Y. et al. Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat. J. Physiol. 561, 65–90 (2004).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).
MICrONS Consortium et al. Functional connectomics spanning multiple areas of mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2021.07.28.454025 (2021).
Yin, W. et al. A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy. Nat. Commun. 11, 4949 (2020).
Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 384, eadk4858 (2024).
Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016).
Földy, C. et al. Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons. Proc. Natl Acad. Sci. USA 113, E5222–E5231 (2016).
Göbel, W. & Helmchen, F. In vivo calcium imaging of neural network function. Physiology 22, 358–365 (2007).
Looger, L. L. & Griesbeck, O. Genetically encoded neural activity indicators. Curr. Opin. Neurobiol. 22, 18–23 (2012).
Knöpfel, T. Genetically encoded optical indicators for the analysis of neuronal circuits. Nat. Rev. Neurosci. 13, 687–700 (2012).
Kim, C. K., Adhikari, A. & Deisseroth, K. Integration of optogenetics with complementary methodologies in systems neuroscience. Nat. Rev. Neurosci. 18, 222–235 (2017).
Yizhar, O., Fenno, L. E., Davidson, T. J., Mogri, M. & Deisseroth, K. Optogenetics in neural systems. Neuron 71, 9–34 (2011).
Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).
Urai, A. E., Doiron, B., Leifer, A. M. & Churchland, A. K. Large-scale neural recordings call for new insights to link brain and behavior. Nat. Neurosci. 25, 11–19 (2022).
Alivisatos, A. P. et al. Nanotools for neuroscience and brain activity mapping. ACS Nano 7, 1850–1866 (2013).
Demas, J. et al. High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy. Nat. Methods 18, 1103–1111 (2021).
Kim, T. H. & Schnitzer, M. J. Fluorescence imaging of large-scale neural ensemble dynamics. Cell 185, 9–41 (2022).
Einevoll, G. T. et al. The scientific case for brain simulations. Neuron 102, 735–744 (2019).
Haufler, D., Ito, S., Koch, C. & Arkhipov, A. Simulations of cortical networks using spatially extended conductance-based neuronal models. J. Physiol. https://doi.org/10.1113/JP284030 (2022).
Koch, C. & Jones, A. Big science, team science, and open science for neuroscience. Neuron 92, 612–616 (2016).
Wiener, M., Sommer, F. T., Ives, Z. G., Poldrack, R. A. & Litt, B. Enabling an open data ecosystem for the neurosciences. Neuron 92, 929 (2016).
Vogelstein, J. T. et al. To the cloud! A grassroots proposal to accelerate brain science discovery. Neuron 92, 622–627 (2016).
Hawrylycz, M. et al. A guide to the BRAIN Initiative Cell census network data ecosystem. PLoS Biol. 21, e3002133 (2023).
Scala, F. et al. Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas. Nat. Commun. 10, 4174 (2019).
Beerens, S., Winterer, J., Lukacsovich, D., Földy, C. & Wozny, C. Transcriptomically-guided pharmacological experiments in neocortical and hippocampal NPY-positive GABAergic interneurons. eNeuro 9, https://doi.org/10.1523/ENEURO.0005-22.2022 (2022).
Scala, F. et al. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature 598, 144–150 (2021).
Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183, 935–953 (2020).
Rudy, B., Fishell, G., Lee, S. & Hjerling-Leffler, J. Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71, 45–61 (2011).
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).
Gray, E. G. Electron microscopy of synaptic contacts on dendrite spines of the cerebral cortex. Nature 183, 1592–1593 (1959).
Colonnier, M. Synaptic patterns on different cell types in the different laminae of the cat visual cortex. An electron microscope study. Brain Res. 9, 268–287 (1968).
Peters, A., Proskauer, C. C. & Ribak, C. E. Chandelier cells in rat visual cortex. J. Comp. Neurol. 206, 397–416 (1982).
Szentágothai, J. & Arbib, M. A. Conceptual models of neural organization. Neurosci. Res. Program Bull. 12, 305–510 (1974).
DeFelipe, J., Hendry, S. H., Jones, E. G. & Schmechel, D. Variability in the terminations of GABAergic chandelier cell axons on initial segments of pyramidal cell axons in the monkey sensory-motor cortex. J. Comp. Neurol. 231, 364–384 (1985).
Fairén, A. & Valverde, F. A specialized type of neuron in the visual cortex of cat: a Golgi and electron microscope study of chandelier cells. J. Comp. Neurol. 194, 761–779 (1980).
Somogyi, P. A specific ‘axo-axonal’ interneuron in the visual cortex of the rat. Brain Res. 136, 345–350 (1977).
Lu, J. et al. Selective inhibitory control of pyramidal neuron ensembles and cortical subnetworks by chandelier cells. Nat. Neurosci. 20, 1377–1383 (2017).
Schneider-Mizell, C. M. et al. Structure and function of axo-axonic inhibition. Elife 10, e73783 (2021).
Seignette, K. et al. Experience shapes chandelier cell function and structure in the visual cortex. Elife 12, RP91153 (2024).
de Lima, A. D. & Morrison, J. H. Ultrastructural analysis of somatostatin-immunoreactive neurons and synapses in the temporal and occipital cortex of the macaque monkey. J. Comp. Neurol. 283, 212–227 (1989).
Meinecke, D. L. & Peters, A. Somatostatin immunoreactive neurons in rat visual cortex: a light and electron microscopic study. J. Neurocytol. 15, 121–136 (1986).
Peters, A. The axon terminals of vasoactive intestinal polypeptide (VIP)-containing bipolar cells in rat visual cortex. J. Neurocytol. 19, 672–685 (1990).
Peters, A., Meinecke, D. L. & Karamanlidis, A. N. Vasoactive intestinal polypeptide immunoreactive neurons in the primary visual cortex of the cat. J. Neurocytol. 16, 23–38 (1987).
Buchanan, J. et al. Oligodendrocyte precursor cells ingest axons in the mouse neocortex. Proc. Natl Acad. Sci. USA 119, e2202580119 (2022).
Bonney, S. K. et al. Public volume electron microscopy data: an essential resource to study the brain microvasculature. Front. Cell Dev. Biol. 10, 849469 (2022).
Schneider-Mizell, C. M. et al. Inhibitory specificity from a connectomic census of mouse visual cortex. Nature (in the press).
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).
Wu, S. J. et al. Cortical somatostatin interneuron subtypes form cell-type-specific circuits. Neuron 111, 2675–2692 (2023).
Gamlin, C. R. et al. Connectomics of predicted Sst transcriptomic types in mouse visual cortex. Nature (in the press).
Holler, S., Köstinger, G., Martin, K. A. C., Schuhknecht, G. F. P. & Stratford, K. J. Structure and function of a neocortical synapse. Nature 591, 111–116 (2021).
Banitt, Y., Martin, K. A. C. & Segev, I. A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression. J. Neurosci. 27, 10230–10239 (2007).
Wang, H. -P., Spencer, D., Fellous, J. -M. & Sejnowski, T. J. Synchrony of thalamocortical inputs maximizes cortical reliability. Science 328, 106–109 (2010).
Millman, D. J. et al. VIP interneurons in mouse primary visual cortex selectively enhance responses to weak but specific stimuli. Elife 9, e55130 (2020).
Keller, A. J. et al. A disinhibitory circuit for contextual modulation in primary visual cortex. Neuron 108, 1181–1193 (2020).
Dipoppa, M. et al. Vision and locomotion shape the interactions between neuron types in mouse visual cortex. Neuron 98, 602–615 (2018).
Mossing, D. P., Veit, J., Palmigiano, A., Miller, K. D. & Adesnik, H. Antagonistic inhibitory subnetworks control cooperation and competition across cortical space. Preprint at bioRxiv https://doi.org/10.1101/2021.03.31.437953 (2021).
Veit, J., Hakim, R., Jadi, M. P., Sejnowski, T. J. & Adesnik, H. Cortical gamma band synchronization through somatostatin interneurons. Nat. Neurosci. 20, 951–959 (2017).
Adesnik, H., Bruns, W., Taniguchi, H., Huang, Z. J. & Scanziani, M. A neural circuit for spatial summation in visual cortex. Nature 490, 226–231 (2012).
Veit, J., Handy, G., Mossing, D. P., Doiron, B. & Adesnik, H. Cortical VIP neurons locally control the gain but globally control the coherence of gamma band rhythms. Neuron 111, 405–417 (2023).
Garrett, M. et al. Experience shapes activity dynamics and stimulus coding of VIP inhibitory cells. Elife 9, e50340 (2020).
Garrett, M. et al. Stimulus novelty uncovers coding diversity in visual cortical circuits. Preprint at bioRxiv https://doi.org/10.1101/2023.02.14.528085 (2023).
Pakan, J. M. et al. Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex. Elife 5, e14985 (2016).
Poort, J. et al. Learning and attention increase visual response selectivity through distinct mechanisms. Neuron 110, 686–697 (2022).
King, C. W., Ledochowitsch, P., Buice, M. A. & de Vries, S. E. J. Saccade-responsive visual cortical neurons do not exhibit distinct visual response properties. eNeuro https://doi.org/10.1523/ENEURO.0051-23.2023 (2023).
Groblewski, P. A. et al. Characterization of learning, motivation, and visual perception in five transgenic mouse lines expressing GCaMP in distinct cell populations. Front. Behav. Neurosci. 14, 104 (2020).
Bugeon, S. et al. A transcriptomic axis predicts state modulation of cortical interneurons. Nature 607, 330–338 (2022).
Condylis, C. et al. Dense functional and molecular readout of a circuit hub in sensory cortex. Science 375, eabl5981 (2022).
Close, J. L., Long, B. R. & Zeng, H. Spatially resolved transcriptomics in neuroscience. Nat. Methods 18, 23–25 (2021).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
O’Toole, S. M., Oyibo, H. K. & Keller, G. B. Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses. Neuron 111, 2918–2928.e8 (2023).
Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–154 (1962).
Reid, R. C. & Alonso, J. M. Specificity of monosynaptic connections from thalamus to visual cortex. Nature 378, 281–284 (1995).
Kara, P., Reinagel, P. & Reid, R. C. Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron 27, 635–646 (2000).
Blasdel, G. G. & Salama, G. Voltage-sensitive dyes reveal a modular organization in monkey striate cortex. Nature 321, 579–585 (1986).
Bonhoeffer, T. & Grinvald, A. in Brain Mapping: the Methods (eds. Toga, A. W. & Mazziotta, J. C.) 55–97 (Academic, 1996).
Malach, R., Amir, Y., Harel, M. & Grinvald, A. Relationship between intrinsic connections and functional architecture revealed by optical imaging and in vivo targeted biocytin injections in primate striate cortex. Proc. Natl Acad. Sci. USA 90, 10469–10473 (1993).
Bosking, W. H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997).
Kisvárday, Z. F., Tóth, E., Rausch, M. & Eysel, U. T. Orientation-specific relationship between populations of excitatory and inhibitory lateral connections in the visual cortex of the cat. Cereb. Cortex 7, 605–618 (1997).
Livingstone, M. S. & Hubel, D. H. Specificity of intrinsic connections in primate primary visual cortex. J. Neurosci. 4, 2830–2835 (1984).
Ohki, K., Chung, S., Ch’ng, Y. H., Kara, P. & Reid, R. C. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433, 597–603 (2005).
Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).
Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).
Bock, D. D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).
Lee, W. -C. A. et al. Anatomy and function of an excitatory network in the visual cortex. Nature 532, 370–374 (2016).
Ding, Z. et al. Functional connectomics reveals general wiring rule in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2023.03.13.531369 (2023).
Wertz, A. et al. Single-cell–initiated monosynaptic tracing reveals layer-specific cortical network modules. Science 349, 70–74 (2015).
Rossi, L. F., Harris, K. D. & Carandini, M. Spatial connectivity matches direction selectivity in visual cortex. Nature 588, 648–652 (2020).
Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).
Zhu, D. et al. Fusing DTI and fMRI data: a survey of methods and applications. Neuroimage 102, 184–191 (2014).
Horn, A., Ostwald, D., Reisert, M. & Blankenburg, F. The structural–functional connectome and the default mode network of the human brain. Neuroimage 102, 142–151 (2014).
Mori, S. et al. Diffusion tensor imaging of the developing mouse brain. Magn. Reson. Med. 46, 18–23 (2001).
Jonckers, E., Van Audekerke, J., De Visscher, G., Van der Linden, A. & Verhoye, M. Functional connectivity fMRI of the rodent brain: comparison of functional connectivity networks in rat and mouse. PLoS ONE 6, e18876 (2011).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Yao, S. et al. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex. Nat. Neurosci. 26, 350–364 (2023).
Harris, J. A. et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202 (2019).
Bohland, J. W. et al. A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLoS Comput. Biol. 5, e1000334 (2009).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).
Foster, N. N. et al. The mouse cortico-basal ganglia-thalamic network. Nature 598, 188–194 (2021).
Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100–1114 (2016).
Hunnicutt, B. J. et al. A comprehensive excitatory input map of the striatum reveals novel functional organization. Elife 5, e19103 (2016).
Hunnicutt, B. J. et al. A comprehensive thalamocortical projection map at the mesoscopic level. Nat. Neurosci. 17, 1276–1285 (2014).
Benavidez, N. L. et al. Organization of the inputs and outputs of the mouse superior colliculus. Nat. Commun. 12, 4004 (2021).
Winnubst, J. et al. Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell 179, 268–281 (2019).
Gao, L. et al. Single-neuron projectome of mouse prefrontal cortex. Nat. Neurosci. 25, 515–529 (2022).
Huang, L. et al. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182, 177–188 (2020).
Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).
Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).
Paxinos, G. & Franklin, K. B. J. Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates (Academic, 2019).
Lu, Y. et al. Macaque Brainnetome Atlas: a multifaceted brain map with parcellation, connection, and histology. Sci. Bull. 69, 2241–2259 (2024).
Dong, H. W. & The Allen Institute for Brain Science. The Allen Reference Atlas, (Book + CD-ROM): a Digital Color Brain Atlas of the C57BL/6J Male Mouse (Wiley, 2008).
Ding, S. -L. et al. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524, 3127–3481 (2016).
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 (2020).
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).
Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).
Liu, L. D. et al. Accurate localization of linear probe electrode arrays across multiple brains. eNeuro https://doi.org/10.1523/ENEURO.0241-21.2021 (2021).
Siegle, J. H. et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592, 86–92 (2021).
D’Souza, R. D. et al. Hierarchical and nonhierarchical features of the mouse visual cortical network. Nat. Commun. 13, 503 (2022).
Peters, A. J., Fabre, J. M. J., Steinmetz, N. A., Harris, K. D. & Carandini, M. Striatal activity topographically reflects cortical activity. Nature 591, 420–425 (2021).
Chen, S. et al. Brain-wide neural activity underlying memory-guided movement. Cell 187, 676–691 (2024).
Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).
Li, N., Chen, T. -W., Guo, Z. V., Gerfen, C. R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015).
Inagaki, H. K., Fontolan, L., Romani, S. & Svoboda, K. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 566, 212–217 (2019).
Ye, Z. et al. Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings. Preprint at bioRxiv https://doi.org/10.1101/2023.08.23.554527 (2023).
Schneider, A. et al. Transcriptomic cell type structures in vivo neuronal activity across multiple timescales. Cell Rep. 42, 112318 (2023).
Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).
Bezaire, M. J., Raikov, I., Burk, K., Vyas, D. & Soltesz, I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. Elife 5, e18566–e18566 (2016).
Ecker, A., Romani, A., Sáray, S., Káli, S. & Migliore, M. Data-driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus 30, 1129–1145 (2020).
Ecker, A. et al. Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3. Elife 11, e71850 (2022).
Potjans, T. C. & Diesmann, M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex 24, 785–806 (2014).
Joglekar, M. R., Mejias, J. F., Yang, G. R. & Wang, X. -J. Inter-areal balanced amplification enhances signal propagation in a large-scale circuit model of the primate cortex. Neuron 98, 222–234 (2018).
Froudist-Walsh, S. et al. A dopamine gradient controls access to distributed working memory in the large-scale monkey cortex. Neuron 109, 3500–3520 (2021).
Teeter, C. et al. Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9, 709 (2018).
Gouwens, N. W. et al. Systematic generation of biophysically detailed models for diverse cortical neuron types. Nat. Commun. 9, 710 (2018).
Billeh, Y. N. et al. Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron 106, 388–403 (2020).
Cai, B. et al. Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations. Preprint at bioRxiv https://doi.org/10.1101/2020.04.21.051268 (2020).
Giacopelli, G., Tegolo, D., Spera, E. & Migliore, M. On the structural connectivity of large-scale models of brain networks at cellular level. Sci. Rep. 11, 4345–4345 (2021).
Stöckl, C., Lang, D. & Maass, W. Probabilistic skeletons endow brain-like neural networks with innate computing capabilities. Preprint at bioRxiv https://doi.org/10.1101/2021.05.18.444689 (2021).
Jabri, T. & MacLean, J. N. Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring. Neural Comput. 34, 2347–2373 (2022).
Chen, G., Scherr, F. & Maass, W. A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing. Sci. Adv. 8, eabq7592 (2022).
Galván Fraile, J. et al. Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations. PLoS Comput. Biol. 20, e1011921 (2024).
Scherr, F. & Maass, W. Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task. Preprint at bioRxiv https://doi.org/10.1101/2021.11.17.469025 (2021).
Rimehaug, A. E. et al. Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. Elife 12, e87169 (2023).
Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).
Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P. & Chichilnisky, E. J. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci. 25, 11003–11013 (2005).
Walker, E. Y. et al. Inception loops discover what excites neurons most using deep predictive models. Nat. Neurosci. 22, 2060–2065 (2019).
Schrimpf, M. et al. Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron 108, 413–423 (2020).
Turishcheva, P. et al. The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos. Preprint at https://arxiv.org/abs/2305.19654 (2023).
Abbott, L. F. et al. The mind of a mouse. Cell 182, 1372–1376 (2020).
Chartrand, T. et al. Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex. Science 382, eadf0805 (2023).
Berg, J. et al. Human neocortical expansion involves glutamatergic neuron diversification. Nature 598, 151–158 (2021).
Kalmbach, B. E. et al. Signature morpho-electric, transcriptomic, and dendritic properties of human layer 5 neocortical pyramidal neurons. Neuron 109, 2914–2927 (2021).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
Lee, B. R. et al. Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science 382, eadf6484 (2023).
Campagnola, L. et al. Local connectivity and synaptic dynamics in mouse and human neocortex. Science 375, eabj5861 (2022).
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H.Z. is on the scientific advisory board of MapLight Therapeutics, Inc. C.K. holds an executive position and has a financial interest in Intrinsic Powers, a company whose purpose is to develop a device that can be used in the clinic to assess the presence and absence of consciousness in patients. This does not pose any conflict of interest with regard to the work undertaken for this publication. All other authors declare no competing interests.
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Arkhipov, A., da Costa, N., de Vries, S. et al. Integrating multimodal data to understand cortical circuit architecture and function. Nat Neurosci 28, 717–730 (2025). https://doi.org/10.1038/s41593-025-01904-7
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DOI: https://doi.org/10.1038/s41593-025-01904-7
