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Biology subjects

JI, Z.

Publications and source records attributed to JI, Z..

2 recordsLinked to original sources

Theta sweeps accelerate cognitive map learning by broadcasting information to unvisited regions

To support flexible behavior, animals need to learn map like representations of environments from limited experiences, but the underlying neural mechanisms remain unclear. Here, we propose that theta sweeps, a prominent feature in the activities of grid and place cells during active movement, can broadcast acquired sensory information to swept unvisited regions, accelerating cognitive map learning. To test this idea, we built a network of direction, grid, and place cell modules, in which theta modulation and firing rate adaptation generate theta sweeps, while Hebbian plasticity enables place cells to bind environmental observations. Simulations and ablations showed that the dorsal ventral grid organization produces multiscale, independent sweeps broadcasting acquired sensory information to a broad, fan shaped unvisited area, enabling unvisited regions to build place environment associations. The model further suggests that declining theta power as the environment becomes familiar acts as an annealing process, shifting from learning general information about the environment to making local refinements. The model makes three testable predictions: strong theta modulation accelerates early learning, environments with broader spatial correlations favor longer theta sweeps, and theta sweeps progressively shorten as environments become familiar. Together, our results support theta sweeps as a mechanism for accelerating cognitive map learning from limited explorations, and it sheds light on developing brain inspired algorithms for building world models.

neuroscience↗

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants

Only a small fraction of annotated plant genes possesses experimentally validated associations with specific phenotypes. Phenotype associated genes have distinct structural, molecular, and evolutionary characteristics compared to non-validated gene models. Here, we developed a simple classifier that uses sequence and evolutionary features which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of loss of function mutations. A model trained solely on genes from maize (Zea mays) identified and prioritized rice (Oryza sativa) and arabidopsis (Arabidopsis thaliana) genes that were highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes displayed patterns consistent with known biological properties of phenotype associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation did not consist exclusively of well-characterized gene families, but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetics efforts and potentially accelerating gene discovery and functional annotation in crops. Significance StatementAnnotation of an organisms genome produces tens of thousands of predicted genes, termed gene models. However, only a few of these have been associated with phenotypes. We built a simple algorithm that uses DNA sequences and evolutionary conservation-based information to predict which genes are most likely to change a plants phenotype when disrupted. In maize, it separates known phenotype-associated genes from other gene models; it also works in rice and arabidopsis. High-scoring genes show signals of biological importance and include many with no known function. This algorithm provides a shortlist of candidate for gene characterization and crop improvement.

plant biology↗