Pub Date : 2024-09-20DOI: 10.1038/s43588-024-00691-8
Yicheng Gao, Qi Liu
A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.
{"title":"Delineating cell types with transcriptional kinetics","authors":"Yicheng Gao, Qi Liu","doi":"10.1038/s43588-024-00691-8","DOIUrl":"10.1038/s43588-024-00691-8","url":null,"abstract":"A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"657-658"},"PeriodicalIF":12.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1038/s43588-024-00688-3
Shenghao Wu, Chengcheng Huang, Adam C. Snyder, Matthew A. Smith, Brent Doiron, Byron M. Yu
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity’s dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function. An automatic framework, SNOPS, is developed for configuring a spiking network model to reproduce neuronal recordings. It is used to discover previously unknown limitations of spiking network models, thereby guiding model development.
{"title":"Automated customization of large-scale spiking network models to neuronal population activity","authors":"Shenghao Wu, Chengcheng Huang, Adam C. Snyder, Matthew A. Smith, Brent Doiron, Byron M. Yu","doi":"10.1038/s43588-024-00688-3","DOIUrl":"10.1038/s43588-024-00688-3","url":null,"abstract":"Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity’s dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function. An automatic framework, SNOPS, is developed for configuring a spiking network model to reproduce neuronal recordings. It is used to discover previously unknown limitations of spiking network models, thereby guiding model development.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"690-705"},"PeriodicalIF":12.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s43588-024-00690-9
Jie Hu
A computational model is proposed to provide a better understanding of human altruism, highlighting the role of multiple motives that influence altruistic behaviors.
为了更好地理解人类的利他主义，我们提出了一个计算模型，强调影响利他行为的多种动机的作用。
{"title":"Deconstructing the compounds of altruism","authors":"Jie Hu","doi":"10.1038/s43588-024-00690-9","DOIUrl":"10.1038/s43588-024-00690-9","url":null,"abstract":"A computational model is proposed to provide a better understanding of human altruism, highlighting the role of multiple motives that influence altruistic behaviors.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"655-656"},"PeriodicalIF":12.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s43588-024-00685-6
Xiaoyan Wu, Xiangjuan Ren, Chao Liu, Hang Zhang
Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and individuals proves challenging if we limit ourselves to one or two motives. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N = 157) and one preregistered online replication (N = 1,258), across 100 different situations, we found that both third-party punishment and third-party helping behaviors (that is, an unaffected individual punishes the transgressor or helps the victim) aligned best with a model of seven socioeconomic motives, referred to as a motive cocktail. For instance, the inequality discounting motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for the differences in intervention willingness between second parties (victims) and third parties, and between punishment and helping. The authors find, through experimental data and computational modeling, that altruistic acts stem from a motive cocktail of up to seven social and economic motives, whose strengths explain distinct behavior patterns across individuals and situations.
{"title":"The motive cocktail in altruistic behaviors","authors":"Xiaoyan Wu, Xiangjuan Ren, Chao Liu, Hang Zhang","doi":"10.1038/s43588-024-00685-6","DOIUrl":"10.1038/s43588-024-00685-6","url":null,"abstract":"Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and individuals proves challenging if we limit ourselves to one or two motives. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N = 157) and one preregistered online replication (N = 1,258), across 100 different situations, we found that both third-party punishment and third-party helping behaviors (that is, an unaffected individual punishes the transgressor or helps the victim) aligned best with a model of seven socioeconomic motives, referred to as a motive cocktail. For instance, the inequality discounting motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for the differences in intervention willingness between second parties (victims) and third parties, and between punishment and helping. The authors find, through experimental data and computational modeling, that altruistic acts stem from a motive cocktail of up to seven social and economic motives, whose strengths explain distinct behavior patterns across individuals and situations.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"659-676"},"PeriodicalIF":12.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00685-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s43588-024-00657-w
Yandong Li, Francesco Monticone
Optical and wave-based computing is attracting renewed interest, motivated by the need for new platforms for resource-intensive special-purpose processing tasks. Here, we discuss whether, why, and how metamaterials and metasurfaces could contribute to achieving an ‘optical advantage’ in computing.
{"title":"Exploring the role of metamaterials in achieving advantage in optical computing","authors":"Yandong Li, Francesco Monticone","doi":"10.1038/s43588-024-00657-w","DOIUrl":"10.1038/s43588-024-00657-w","url":null,"abstract":"Optical and wave-based computing is attracting renewed interest, motivated by the need for new platforms for resource-intensive special-purpose processing tasks. Here, we discuss whether, why, and how metamaterials and metasurfaces could contribute to achieving an ‘optical advantage’ in computing.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"545-548"},"PeriodicalIF":12.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s43588-024-00669-6
Keith A. Brown, Grace X. Gu
Additive manufacturing plays an essential role in producing metamaterials by precisely controlling geometries and multiscale structures to achieve the desired properties. In this Comment, we highlight the challenges and opportunities from additive manufacturing for computational metamaterials design.
{"title":"Computational challenges in additive manufacturing for metamaterials design","authors":"Keith A. Brown, Grace X. Gu","doi":"10.1038/s43588-024-00669-6","DOIUrl":"10.1038/s43588-024-00669-6","url":null,"abstract":"Additive manufacturing plays an essential role in producing metamaterials by precisely controlling geometries and multiscale structures to achieve the desired properties. In this Comment, we highlight the challenges and opportunities from additive manufacturing for computational metamaterials design.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"553-555"},"PeriodicalIF":12.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s43588-024-00672-x
Silvia Bonfanti, Stefan Hiemer, Raja Zulkarnain, Roberto Guerra, Michael Zaiser, Stefano Zapperi
In the past few years, design of mechanical metamaterials has been empowered by computational tools that have allowed the community to overcome limitations of human intuition. By leveraging efficient optimization algorithms and computational physics models, it is now possible to explore vast design spaces, achieving new material functionalities with unprecedented performance. Here, we present our viewpoint on the state of the art of computational metamaterials design, discussing recent advances in topology optimization and machine learning design with respect to challenges in additive manufacturing. Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.
{"title":"Computational design of mechanical metamaterials","authors":"Silvia Bonfanti, Stefan Hiemer, Raja Zulkarnain, Roberto Guerra, Michael Zaiser, Stefano Zapperi","doi":"10.1038/s43588-024-00672-x","DOIUrl":"10.1038/s43588-024-00672-x","url":null,"abstract":"In the past few years, design of mechanical metamaterials has been empowered by computational tools that have allowed the community to overcome limitations of human intuition. By leveraging efficient optimization algorithms and computational physics models, it is now possible to explore vast design spaces, achieving new material functionalities with unprecedented performance. Here, we present our viewpoint on the state of the art of computational metamaterials design, discussing recent advances in topology optimization and machine learning design with respect to challenges in additive manufacturing. Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"574-583"},"PeriodicalIF":12.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s43588-024-00687-4
This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.
{"title":"Metamaterials design via and for computation","authors":"","doi":"10.1038/s43588-024-00687-4","DOIUrl":"10.1038/s43588-024-00687-4","url":null,"abstract":"This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"543-544"},"PeriodicalIF":12.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00687-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s43588-024-00673-w
Qiguang He, Samuele Ferracin, Jordan R. Raney
Unconventional computing based on mechanical metamaterials has been of growing interest, including how such metamaterials might process information via autonomous interactions with their environment. Here we describe recent efforts to combine responsive materials with nonlinear mechanical metamaterials to achieve stimuli-responsive mechanical logic and computation. We also describe some key challenges and opportunities in the design and construction of these devices, including the lack of comprehensive computational tools, and the challenges associated with patterning multi-material mechanisms. Mechanical metamaterials have shown potential for processing information via autonomous environmental interactions. This Perspective summarizes recent efforts and challenges on integrating stimuli-responsive materials with mechanical metamaterials for mechanical computing, and explores the remaining challenges in the field.
{"title":"Programmable responsive metamaterials for mechanical computing and robotics","authors":"Qiguang He, Samuele Ferracin, Jordan R. Raney","doi":"10.1038/s43588-024-00673-w","DOIUrl":"10.1038/s43588-024-00673-w","url":null,"abstract":"Unconventional computing based on mechanical metamaterials has been of growing interest, including how such metamaterials might process information via autonomous interactions with their environment. Here we describe recent efforts to combine responsive materials with nonlinear mechanical metamaterials to achieve stimuli-responsive mechanical logic and computation. We also describe some key challenges and opportunities in the design and construction of these devices, including the lack of comprehensive computational tools, and the challenges associated with patterning multi-material mechanisms. Mechanical metamaterials have shown potential for processing information via autonomous environmental interactions. This Perspective summarizes recent efforts and challenges on integrating stimuli-responsive materials with mechanical metamaterials for mechanical computing, and explores the remaining challenges in the field.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"567-573"},"PeriodicalIF":12.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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