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1. Predictive models of genetic redundancy in Arabidopsis thaliana

   https://doi.org/10.1093/molbev/msab111  

   Cusack, Siobhan A; Wang, Peipei; Lotreck, Serena G; Moore, Bethany M; Meng, Fanrui; Conner, Jeffrey K; Krysan, Patrick J; Lehti-Shiu, Melissa D; Shiu, Shin-Han (April 2021, Molecular Biology and Evolution)

   de Meaux, Juliette (Ed.)

   Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including post-translational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on hold-out, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies. 

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2. Functional connectomics spanning multiple areas of mouse visual cortex

   https://doi.org/10.1038/s41586-025-08790-w  

   Bae, J Alexander; Baptiste, Mahaly; Baptiste, Maya R; Bishop, Caitlyn A; Bodor, Agnes L; Brittain, Derrick; Brooks, Victoria; Buchanan, JoAnn; Bumbarger, Daniel J; Castro, Manuel A; et al (April 2025, Nature)

   Abstract Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis^1,2. Accompanying studies describe its use for comprehensive characterization of cell types^3–6, a synaptic level connectivity diagram of a cortical column⁴, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data^4,7. Functionally, we identify new computational principles of how information is integrated across visual space⁸, characterize novel types of neuronal invariances⁹and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas^10,11. 

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3. Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks

   Liu, Ziming; Khan, Mikail; Fiete, Ila R.; Tegmark, Max (December 2023, NeurIPS 2023 Workshop NeurReps)

   Recurrent neural networks (RNNs) trained on a diverse ensemble of cognitive tasks, as described by Yang et al. (2019); Khona et al. (2023), have been shown to exhibit functional modularity, where neurons organize into discrete functional clusters, each specialized for specific shared computational subtasks. However, these RNNs do not demonstrate anatomical modularity, where these functionally specialized clusters also have a distinct spatial organization. This contrasts with the human brain which has both functional and anatomical modularity. Is there a way to train RNNs to make them more like brains in this regard? We apply a recent machine learning method, brain-inspired modular training (BIMT), to encourage neural connectivity to be local in space. Consequently, hidden neuron organization of the RNN forms spatial structures reminiscent of those of the brain: spatial clusters which correspond to functional clusters. Compared to standard L1 regularization and absence of regularization, BIMT exhibits superior performance by optimally balancing between task performance and sparsity. This balance is quantified both in terms of the number of active neurons and the cumulative wiring length. In addition to achieving brain-like organization in RNNs, our findings also suggest that BIMT holds promise for applications in neuromorphic computing and enhancing the interpretability of neural network architectures. 

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4. Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making

   https://doi.org/10.1038/s41593-022-01245-9  

   Musall, Simon; Sun, Xiaonan R; Mohan, Hemanth; An, Xu; Gluf, Steven; Li, Shu-Jing; Drewes, Rhonda; Cravo, Emma; Lenzi, Irene; Yin, Chaoqun; et al (January 2023, Nature Neuroscience)

   Abstract Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract, intratelencephalic and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scales. Cortical activity and optogenetic inactivation during an auditory decision task revealed distinct functional roles. All PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, pyramidal tract neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions. 

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5. Glial Draper signaling triggers cross-neuron plasticity in bystander neurons after neuronal cell death in Drosophila

   https://doi.org/10.1038/s41467-023-40142-y  

   Wang, Yupu; Zhang, Ruiling; Huang, Sihao; Valverde, Parisa Tajalli Tehrani; Lobb-Rabe, Meike; Ashley, James; Venkatasubramanian, Lalanti; Carrillo, Robert A. (July 2023, Nature Communications)

   Abstract Neuronal cell death and subsequent brain dysfunction are hallmarks of aging and neurodegeneration, but how the nearby healthy neurons (bystanders) respond to the death of their neighbors is not fully understood. In theDrosophilalarval neuromuscular system, bystander motor neurons can structurally and functionally compensate for the loss of their neighbors by increasing their terminal bouton number and activity. We term this compensation as cross-neuron plasticity, and in this study, we demonstrate that theDrosophilaengulfment receptor, Draper, and the associated kinase, Shark, are required for cross-neuron plasticity. Overexpression of the Draper-I isoform boosts cross-neuron plasticity, implying that the strength of plasticity correlates with Draper signaling. In addition, we find that functional cross-neuron plasticity can be induced at different developmental stages. Our work uncovers a role for Draper signaling in cross-neuron plasticity and provides insights into how healthy bystander neurons respond to the loss of their neighboring neurons. 

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