Andreas Tolias

All Publications

• Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates. Cell reports Fu, J., Pierzchlewicz, P. A., Willeke, K. F., Bashiri, M., Muhammad, T., Diamantaki, M., Froudarakis, E., Restivo, K., Ponder, K., Denfield, G. H., Sinz, F., Tolias, A. S., Franke, K. 2024; 43 (8): 114639

  Abstract

  A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF. This heterogeneity correlated with deviations from optimal Gabor filters and was consistent across cortical layers and recording modalities (calcium vs. spikes). In contrast, model-synthesized MEIs for macaque V1 neurons were predominantly Gabor like, consistent with previous studies. These findings suggest that complex spatial feature selectivity emerges earlier in the visual pathway in mice than in primates. This may provide a faster, though less general, method of extracting task-relevant information.

  View details for DOI 10.1016/j.celrep.2024.114639

  View details for PubMedID 39167488

• The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos. ArXiv Turishcheva, P., Fahey, P. G., Vystrčilová, M., Hansel, L., Froebe, R., Ponder, K., Qiu, Y., Willeke, K. F., Bashiri, M., Wang, E., Ding, Z., Tolias, A. S., Sinz, F. H., Ecker, A. S. 2024

  Abstract

  Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.

  View details for DOI 10.1101/2023.03.15.532836v1

  View details for PubMedID 37396602

  View details for PubMedCentralID PMC10312815

• Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos. ArXiv Turishcheva, P., Fahey, P. G., Vystrčilová, M., Hansel, L., Froebe, R., Ponder, K., Qiu, Y., Willeke, K. F., Bashiri, M., Baikulov, R., Zhu, Y., Ma, L., Yu, S., Huang, T., Li, B. M., Wulf, W. D., Kudryashova, N., Hennig, M. H., Rochefort, N. L., Onken, A., Wang, E., Ding, Z., Tolias, A. S., Sinz, F. H., Ecker, A. S. 2024

  Abstract

  Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at www.sensorium-competition.net.

  View details for DOI 10.1101/2023.03.15.532836v1

  View details for PubMedID 39040641

  View details for PubMedCentralID PMC11261979

• Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky. bioRxiv : the preprint server for biology Franke, K., Cai, C., Ponder, K., Fu, J., Sokoloski, S., Berens, P., Tolias, A. S. 2024

  Abstract

  Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

  View details for DOI 10.1101/2023.06.01.543054

  View details for PubMedID 37333280

  View details for PubMedCentralID PMC10274736

• Grand Challenges at the Interface of Engineering and Medicine. IEEE open journal of engineering in medicine and biology Subramaniam, S., Akay, M., Anastasio, M. A., Bailey, V., Boas, D., Bonato, P., Chilkoti, A., Cochran, J. R., Colvin, V., Desai, T. A., Duncan, J. S., Epstein, F. H., Fraley, S., Giachelli, C., Grande-Allen, K. J., Green, J., Guo, X. E., Hilton, I. B., Humphrey, J. D., Johnson, C. R., Karniadakis, G., King, M. R., Kirsch, R. F., Kumar, S., Laurencin, C. T., Li, S., Lieber, R. L., Lovell, N., Mali, P., Margulies, S. S., Meaney, D. F., Ogle, B., Palsson, B., A Peppas, N., Perreault, E. J., Rabbitt, R., Setton, L. A., Shea, L. D., Shroff, S. G., Shung, K., Tolias, A. S., van der Meulen, M. C., Varghese, S., Vunjak-Novakovic, G., White, J. A., Winslow, R., Zhang, J., Zhang, K., Zukoski, C., Miller, M. I. 2024; 5: 1-13

  Abstract

  Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

  View details for DOI 10.1109/OJEMB.2024.3351717

  View details for PubMedID 38415197

• Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature communications Zador, A., Escola, S., Richards, B., Olveczky, B., Bengio, Y., Boahen, K., Botvinick, M., Chklovskii, D., Churchland, A., Clopath, C., DiCarlo, J., Ganguli, S., Hawkins, J., Kording, K., Koulakov, A., LeCun, Y., Lillicrap, T., Marblestone, A., Olshausen, B., Pouget, A., Savin, C., Sejnowski, T., Simoncelli, E., Solla, S., Sussillo, D., Tolias, A. S., Tsao, D. 2023; 14 (1): 1597

  Abstract

  Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

  View details for DOI 10.1038/s41467-023-37180-x

  View details for PubMedID 36949048

• Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy. Cell Liu, Z., Lu, X., Villette, V., Gou, Y., Colbert, K. L., Lai, S., Guan, S., Land, M. A., Lee, J., Assefa, T., Zollinger, D. R., Korympidou, M. M., Vlasits, A. L., Pang, M. M., Su, S., Cai, C., Froudarakis, E., Zhou, N., Patel, S. S., Smith, C. L., Ayon, A., Bizouard, P., Bradley, J., Franke, K., Clandinin, T. R., Giovannucci, A., Tolias, A. S., Reimer, J., Dieudonne, S., St-Pierre, F. 2022

  Abstract

  Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400mum and report voltage correlations in pairs of neurons.

  View details for DOI 10.1016/j.cell.2022.07.013

  View details for PubMedID 35985322

• Increased reliability of visually-evoked activity in area V1 of the MECP2-duplication mouse model of autism. The Journal of neuroscience : the official journal of the Society for Neuroscience Ash, R. T., Palagina, G., Fernandez-Leon, J. A., Park, J., Seilheimer, R., Lee, S., Sabharwal, J., Reyes, F., Wang, J., Lu, D., Sarfraz, M., Froudarakis, E., Tolias, A. S., Wu, S. M., Smirnakis, S. M. 2022

  Abstract

  Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in layer 2/3 neurons of adult male and female primary visual cortex in the MECP2-duplication syndrome animal model of autism. Increased response reliability was due in part to decreased response amplitude, decreased fluctuations in endogenous activity, and an abnormal decoupling of visual-evoked activity from endogenous activity. Similar to what was observed neuronally, the optokinetic reflex occurred more reliably at low contrasts in mutant mice compared to controls. Retinal responses did not explain our observations. These data suggest that the circuit mechanisms for combining sensory-evoked and endogenous signal and noise processes may be altered in this form of syndromic autism.SIGNIFICANT STATEMENT:Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in primary visual cortex of the animal model for MECP2-duplication syndrome, a high-penetrance single-gene cause of autism. Visual-evoked activity was abnormally decoupled from endogenous activity in mutant mice, suggesting in line with the influential "hypo-priors" theory of autism that sensory priors embedded in endogenous activity may have less influence on perception in autism.

  View details for DOI 10.1523/JNEUROSCI.0654-22.2022

  View details for PubMedID 35831173

• A multimodal cell census and atlas of the mammalian primary motor cortex NATURE Callaway, E. M., Dong, H., Ecker, J. R., Hawrylycz, M. J., Huang, Z., Lein, E. S., Ngai, J., Osten, P., Ren, B., Tolias, A., White, O., Zeng, H., Zhuang, X., Ascoli, G. A., Behrens, M., Chun, J., Feng, G., Gee, J. C., Ghosh, S. S., Halchenko, Y. O., Hertzano, R., Lim, B., Martone, M. E., Ng, L., Pachter, L., Ropelewski, A. J., Tickle, T. L., Yang, X., Zhang, K., Bakken, T. E., Berens, P., Daigle, T. L., Harris, J. A., Jorstad, N. L., Kalmbach, B. E., Kobak, D., Li, Y., Liu, H., Matho, K. S., Mukamel, E. A., Naeemi, M., Scala, F., Tan, P., Ting, J. T., Xie, F., Zhang, M., Zhang, Z., Zhou, J., Zingg, B., Bertagnolli, D., Casper, T., Crichton, K., Dee, N., Diep, D., Ding, S., Dong, W., Dougherty, E. L., Fong, O., Goldman, M., Goldy, J., Hodge, R. D., Hu, L., Keene, C., Krienen, F. M., Kroll, M., Lake, B. B., Lathia, K., Linnarsson, S., Liu, C. S., Macosko, E. Z., McCarroll, S. A., McMillen, D., Nadaf, N. M., Thuc Nghi Nguyen, Palmer, C. R., Thanh Pham, Plongthongkum, N., Reed, N. M., Regev, A., Rimorin, C., Romanow, W. J., Savoia, S., Siletti, K., Smith, K., Sulc, J., Tasic, B., Tieu, M., Torkelson, A., Tung, H., van Velthoven, C. J., Vanderburg, C. R., Yanny, A., Fang, R., Hou, X., Lucero, J. D., Osteen, J. K., Pinto-Duarte, A., Poirion, O., Preissl, S., Wang, X., Aldridge, A., Bartlett, A., Boggeman, L., O'Connor, C., Castanon, R. G., Chen, H., Fitzpatrick, C., Luo, C., Nery, J. R., Nunn, M., Rivkin, A. C., Tian, W., Dominguez, B., Ito-Cole, T., Jacobs, M., Jin, X., Lee, C., Lee, K., Miyazaki, P., Pang, Y., Rashid, M., Smith, J. B., Vu, M., Williams, E., Armand, E., Biancalani, T., Booeshaghi, A., Crow, M., Dudoit, S., Fischer, S., Gillis, J., Hu, Q., Kharchenko, P., Niu, S., Ntranos, V., Purdom, E., Risso, D., de Bezieux, H., Somasundaram, S., Street, K., Svensson, V., Vaishnav, E., Van den Berge, K., Welch, J. D., Yao, Z., An, X., Bateup, H. S., Bowman, I., Chance, R. K., Foster, N. N., Galbavy, W., Gong, H., Gou, L., Hatfield, J. T., Hintiryan, H., Hirokawa, K. E., Kim, G., Kramer, D. J., Li, A., Li, X., Luo, Q., Munoz-Castaneda, R., Stafford, D. A., Feng, Z., Jia, X., Jiang, S., Jiang, T., Kuang, X., Larsen, R., Lesnar, P., Li, Y., Li, Y., Liu, L., Peng, H., Qu, L., Ren, M., Ruan, Z., Shen, E., Song, Y., Wakeman, W., Wang, P., Wang, Y., Wang, Y., Yin, L., Yuan, J., Zhao, S., Zhao, X., Narasimhan, A., Palaniswamy, R., Banerjee, S., Ding, L., Huilgol, D., Huo, B., Kuo, H., Laturnus, S., Li, X., Mitra, P. P., Mizrachi, J., Wang, Q., Xie, P., Xiong, F., Yu, Y., Eichhorn, S. W., Berg, J., Bernabucci, M., Bernaerts, Y., Cadwell, C., Castro, J., Dalley, R., Hartmanis, L., Horwitz, G. D., Jiang, X., Ko, A. L., Miranda, E., Mulherkar, S., Nicovich, P. R., Owen, S. F., Sandberg, R., Sorensen, S. A., Tan, Z., Allen, S., Hockemeyer, D., Lee, A. Y., Veldman, M. B., Adkins, R. S., Ament, S. A., Bravo, H., Carter, R., Chatterjee, A., Colantuoni, C., Crabtree, J., Creasy, H., Felix, V., Giglio, M., Herb, B. R., Kancherla, J., Mahurkar, A., McCracken, C., Nickel, L., Olley, D., Orvis, J., Schor, M., Hood, G., Dichter, B., Grauer, M., Helba, B., Bandrowski, A., Barkas, N., Carlin, B., D'Orazi, F. D., Degatano, K., Gillespie, T. H., Khajouei, F., Konwar, K., Thompson, C., Kelly, K., Mok, S., Sunkin, S., BRAIN Initiative Cell Census Netwo 2021; 598 (7879): 86-102

  Abstract

  Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

  View details for DOI 10.1038/s41586-021-03950-0

  View details for Web of Science ID 000705847500002

  View details for PubMedID 34616075

  View details for PubMedCentralID PMC8494634

• Author Correction: A community-based transcriptomics classification and nomenclature of neocortical cell types. Nature neuroscience Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armananzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., Bystron, I., Capogna, M., Chang, Y., Clemens, A., de Kock, C. P., DeFelipe, J., Dos Santos, S. E., Dunville, K., Feldmeyer, D., Fiath, R., Fishell, G. J., Foggetti, A., Gao, X., Ghaderi, P., Goriounova, N. A., Gunturkun, O., Hagihara, K., Hall, V. J., Helmstaedter, M., Herculano-Houzel, S., Hilscher, M. M., Hirase, H., Hjerling-Leffler, J., Hodge, R., Huang, J., Huda, R., Khodosevich, K., Kiehn, O., Koch, H., Kuebler, E. S., Kuhnemund, M., Larranaga, P., Lelieveldt, B., Louth, E. L., Lui, J. H., Mansvelder, H. D., Marin, O., Martinez-Trujillo, J., Chameh, H. M., Mohapatra, A. N., Munguba, H., Nedergaard, M., Nemec, P., Ofer, N., Pfisterer, U. G., Pontes, S., Redmond, W., Rossier, J., Sanes, J. R., Scheuermann, R. H., Serrano-Saiz, E., Staiger, J. F., Somogyi, P., Tamas, G., Tolias, A. S., Tosches, M. A., Garcia, M. T., Wozny, C., Wuttke, T. V., Liu, Y., Yuan, J., Zeng, H., Lein, E. 2021

  View details for DOI 10.1038/s41593-020-00779-0

  View details for PubMedID 33742182

• Publisher Correction: A community-based transcriptomics classification and nomenclature of neocortical cell types. Nature neuroscience Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Arnedillo, R. A., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., Bystron, I., Capogna, M., Chang, Y., Clemens, A., de Kock, C. P., DeFelipe, J., Dos Santos, S. E., Dunville, K., Feldmeyer, D., Fiath, R., Fishell, G. J., Foggetti, A., Gao, X., Ghaderi, P., Goriounova, N. A., Gunturkun, O., Hagihara, K., Hall, V. J., Helmstaedter, M., Herculano, S., Hilscher, M. M., Hirase, H., Hjerling-Leffler, J., Hodge, R., Huang, J., Huda, R., Khodosevich, K., Kiehn, O., Koch, H., Kuebler, E. S., Kuhnemund, M., Larranaga, P., Lelieveldt, B., Louth, E. L., Lui, J. H., Mansvelder, H. D., Marin, O., Martinez-Trujillo, J., Moradi Chameh, H., Nath, A., Nedergaard, M., Nemec, P., Ofer, N., Pfisterer, U. G., Pontes, S., Redmond, W., Rossier, J., Sanes, J. R., Scheuermann, R., Serrano-Saiz, E., Steiger, J. F., Somogyi, P., Tamas, G., Tolias, A. S., Tosches, M. A., Garcia, M. T., Vieira, H. M., Wozny, C., Wuttke, T. V., Yong, L., Yuan, J., Zeng, H., Lein, E. 2020

  Abstract

  A Correction to this paper has been published: 10.1038/s41593-020-00768-3.

  View details for DOI 10.1038/s41593-020-00768-3

  View details for PubMedID 33277642

• Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time. Neuron Moreaux, L. C., Yatsenko, D., Sacher, W. D., Choi, J., Lee, C., Kubat, N. J., Cotton, R. J., Boyden, E. S., Lin, M. Z., Tian, L., Tolias, A. S., Poon, J. K., Shepard, K. L., Roukes, M. L. 2020; 108 (1): 66–92

  Abstract

  We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm3 volume of mouse cortex comprising 100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.

  View details for DOI 10.1016/j.neuron.2020.09.043

  View details for PubMedID 33058767

• A community-based transcriptomics classification and nomenclature of neocortical cell types. Nature neuroscience Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Arnedillo, R. A., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., Bystron, I., Capogna, M., Chang, Y., Clemens, A., de Kock, C. P., DeFelipe, J., Dos Santos, S. E., Dunville, K., Feldmeyer, D., Fiath, R., Fishell, G. J., Foggetti, A., Gao, X., Ghaderi, P., Goriounova, N. A., Gunturkun, O., Hagihara, K., Hall, V. J., Helmstaedter, M., Herculano, S., Hilscher, M. M., Hirase, H., Hjerling-Leffler, J., Hodge, R., Huang, J., Huda, R., Khodosevich, K., Kiehn, O., Koch, H., Kuebler, E. S., Kuhnemund, M., Larranaga, P., Lelieveldt, B., Louth, E. L., Lui, J. H., Mansvelder, H. D., Marin, O., Martinez-Trujillo, J., Moradi Chameh, H., Nath, A., Nedergaard, M., Nemec, P., Ofer, N., Pfisterer, U. G., Pontes, S., Redmond, W., Rossier, J., Sanes, J. R., Scheuermann, R., Serrano-Saiz, E., Steiger, J. F., Somogyi, P., Tamas, G., Tolias, A. S., Tosches, M. A., Garcia, M. T., Vieira, H. M., Wozny, C., Wuttke, T. V., Yong, L., Yuan, J., Zeng, H., Lein, E. 2020

  Abstract

  To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

  View details for DOI 10.1038/s41593-020-0685-8

  View details for PubMedID 32839617

• Patterned photostimulation via visible-wavelength photonic probes for deep brain optogenetics. Neurophotonics Segev, E., Reimer, J., Moreaux, L. C., Fowler, T. M., Chi, D., Sacher, W. D., Lo, M., Deisseroth, K., Tolias, A. S., Faraon, A., Roukes, M. L. 2017; 4 (1): 011002-?

  Abstract

  Optogenetic methods developed over the past decade enable unprecedented optical activation and silencing of specific neuronal cell types. However, light scattering in neural tissue precludes illuminating areas deep within the brain via free-space optics; this has impeded employing optogenetics universally. Here, we report an approach surmounting this significant limitation. We realize implantable, ultranarrow, silicon-based photonic probes enabling the delivery of complex illumination patterns deep within brain tissue. Our approach combines methods from integrated nanophotonics and microelectromechanical systems, to yield photonic probes that are robust, scalable, and readily producible en masse. Their minute cross sections minimize tissue displacement upon probe implantation. We functionally validate one probe design in vivo with mice expressing channelrhodopsin-2. Highly local optogenetic neural activation is demonstrated by recording the induced response-both by extracellular electrical recordings in the hippocampus and by two-photon functional imaging in the cortex of mice coexpressing GCaMP6.

  View details for PubMedID 27990451

• Highly Multiplexed Nanophotonic Probes With Independently Controllable Emitters for Optogenetic Brain Stimulation Segev, E., Moreaux, L. C., Reimer, J., Fowler, T. M., Chi, D., Sacher, W. D., Lo, M., Deisseroth, K., Tolias, A. S., Faraon, A., Roukes, M. L., IEEE IEEE. 2016

  View details for Web of Science ID 000391286401348

• Lack of long-term cortical reorganization after macaque retinal lesions NATURE Smirnakis, S. M., Brewer, A. A., Schmid, M. C., Tolias, A. S., Schuz, A., Augath, M., Inhoffen, W., Wandell, B. A., Logothetis, N. K. 2005; 435 (7040): 300-307

  Abstract

  Several aspects of cortical organization are thought to remain plastic into adulthood, allowing cortical sensorimotor maps to be modified continuously by experience. This dynamic nature of cortical circuitry is important for learning, as well as for repair after injury to the nervous system. Electrophysiology studies suggest that adult macaque primary visual cortex (V1) undergoes large-scale reorganization within a few months after retinal lesioning, but this issue has not been conclusively settled. Here we applied the technique of functional magnetic resonance imaging (fMRI) to detect changes in the cortical topography of macaque area V1 after binocular retinal lesions. fMRI allows non-invasive, in vivo, long-term monitoring of cortical activity with a wide field of view, sampling signals from multiple neurons per unit cortical area. We show that, in contrast with previous studies, adult macaque V1 does not approach normal responsivity during 7.5 months of follow-up after retinal lesions, and its topography does not change. Electrophysiology experiments corroborated the fMRI results. This indicates that adult macaque V1 has limited potential for reorganization in the months following retinal injury.

  View details for DOI 10.1038/nature03495

  View details for Web of Science ID 000229185000036

  View details for PubMedID 15902248