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1. Beyond Cognitive Templates: Re-Examining Template Metaphors Used for Animal Recognition and Navigation

   https://doi.org/10.1093/icb/icab040  

   Sung, Jenny Y; Harris, Olivia K; Hensley, Nicholai M; Chemero, Anthony P; Morehouse, Nathan I (May 2021, Integrative and Comparative Biology)

   Synopsis The term “cognitive template” originated from work in human-based cognitive science to describe a literal, stored, neural representation used in recognition tasks. As the study of cognition has expanded to nonhuman animals, the term has diffused to describe a wider range of animal cognitive tools and strategies that guide action through the recognition of and discrimination between external states. One potential reason for this nonstandardized meaning and variable employment is that researchers interested in the broad range of animal recognition tasks enjoy the simplicity of the cognitive template concept and have allowed it to become shorthand for many dissimilar or unknown neural processes without deep scrutiny of how this metaphor might comport with underlying neurophysiology. We review the functional evidence for cognitive templates in fields such as perception, navigation, communication, and learning, highlighting any neural correlates identified by these studies. We find that the concept of cognitive templates has facilitated valuable exploration at the interface between animal behavior and cognition, but the quest for a literal template has failed to attain mechanistic support at the level of neurophysiology. This may be the result of a misled search for a single physical locus for the “template” itself. We argue that recognition and discrimination processes are best treated as emergent and, as such, may not be physically localized within single structures of the brain. Rather, current evidence suggests that such tasks are accomplished through synergies between multiple distributed processes in animal nervous systems. We thus advocate for researchers to move toward a more ecological, process-oriented conception, especially when discussing the neural underpinnings of recognition-based cognitive tasks. 

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2. CaImAn an open source tool for scalable calcium imaging data analysis

   https://doi.org/10.7554/eLife.38173  

   Giovannucci, Andrea; Friedrich, Johannes; Gunn, Pat; Kalfon, Jérémie; Brown, Brandon L; Koay, Sue Ann; Taxidis, Jiannis; Najafi, Farzaneh; Gauthier, Jeffrey L; Zhou, Pengcheng; et al (January 2019, eLife)

   The human brain contains billions of cells called neurons that rapidly carry information from one part of the brain to another. Progress in medical research and healthcare is hindered by the difficulty in understanding precisely which neurons are active at any given time. New brain imaging techniques and genetic tools allow researchers to track the activity of thousands of neurons in living animals over many months. However, these experiments produce large volumes of data that researchers currently have to analyze manually, which can take a long time and generate irreproducible results. There is a need to develop new computational tools to analyze such data. The new tools should be able to operate on standard computers rather than just specialist equipment as this would limit the use of the solutions to particularly well-funded research teams. Ideally, the tools should also be able to operate in real-time as several experimental and therapeutic scenarios, like the control of robotic limbs, require this. To address this need, Giovannucci et al. developed a new software package called CaImAn to analyze brain images on a large scale. Firstly, the team developed algorithms that are suitable to analyze large sets of data on laptops and other standard computing equipment. These algorithms were then adapted to operate online in real-time. To test how well the new software performs against manual analysis by human researchers, Giovannucci et al. asked several trained human annotators to identify active neurons that were round or donut-shaped in several sets of imaging data from mouse brains. Each set of data was independently analyzed by three or four researchers who then discussed any neurons they disagreed on to generate a ‘consensus annotation’. Giovannucci et al. then used CaImAn to analyze the same sets of data and compared the results to the consensus annotations. This demonstrated that CaImAn is nearly as good as human researchers at identifying active neurons in brain images. CaImAn provides a quicker method to analyze large sets of brain imaging data and is currently used by over a hundred laboratories across the world. The software is open source, meaning that it is freely-available and that users are encouraged to customize it and collaborate with other users to develop it further. 

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3. From fossils to mind

   https://doi.org/10.1038/s42003-023-04803-4  

   de_Sousa, Alexandra A; Beaudet, Amélie; Calvey, Tanya; Bardo, Ameline; Benoit, Julien; Charvet, Christine J; Dehay, Colette; Gómez-Robles, Aida; Gunz, Philipp; Heuer, Katja; et al (December 2023, Communications Biology)

   Abstract Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology’s approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior. 

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4. Efficient federated learning for distributed neuroimaging data

   https://doi.org/10.3389/fninf.2024.1430987  

   Thapaliya, Bishal; Ohib, Riyasat; Geenjaar, Eloy; Liu, Jingyu; Calhoun, Vince; Plis, Sergey M (September 2024, Frontiers in Neuroinformatics)

   Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset. 

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5. Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future

   https://doi.org/10.1002/pld3.109  

   Doherty, Colleen; Friesner, Joanna; Gregory, Brian; Loraine, Ann; Megraw, Molly; Meyers, Blake C; Provart, Nicholas; Slotkin, R Keith; Town, Chris; Assmann, Sarah M; et al (January 2019, Plant Direct)

   Abstract Effective research, education, and outreach efforts by theArabidopsis thalianacommunity, as well as other scientific communities that depend on Arabidopsis resources, depend vitally on easily available and publicly‐shared resources. These resources include reference genome sequence data and an ever‐increasing number of diverse data sets and data types.TAIR(The Arabidopsis Information Resource) and Araport (originally named the Arabidopsis Information Portal) are community informatics resources that provide tools, data, and applications to the more than 30,000 researchers worldwide that use in their work either Arabidopsis as a primary system of study or data derived from Arabidopsis. Four years after Araport's establishment, theIAICheld another workshop to evaluate the current status of Arabidopsis Informatics and chart a course for future research and development. The workshop focused on several challenges, including the need for reliable and current annotation, community‐defined common standards for data and metadata, and accessible and user‐friendly repositories/tools/methods for data integration and visualization. Solutions envisioned included (a) a centralized annotation authority to coalesce annotation from new groups, establish a consistent naming scheme, distribute this format regularly and frequently, and encourage and enforce its adoption. (b) Standards for data and metadata formats, which are essential, but challenging when comparing across diverse genotypes and in areas with less‐established standards (e.g., phenomics, metabolomics). Community‐established guidelines need to be developed. (c) A searchable, central repository for analysis and visualization tools. Improved versioning and user access would make tools more accessible. Workshop participants proposed a “one‐stop shop” website, an Arabidopsis “Super‐Portal” to link tools, data resources, programmatic standards, and best practice descriptions for each data type. This must have community buy‐in and participation in its establishment and development to encourage adoption. 

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