[Re] How learning can guide evolution
An R replication of a model of how learning can guide evolution, associated with the Baldwin effect.
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An R replication of a model of how learning can guide evolution, associated with the Baldwin effect.
A replication about calibration and predictive uncertainty in deep neural networks, including mixup in its source keywords.
A Julia replication of an ecological population model concerning regulation by insect natural enemies.
A replication about signal propagation and balanced amplification in a large-scale primate-cortex circuit model; the metadata identifies NEST.
A replication about graph representations and fairness-aware data augmentation, with separate upstream code and data records.
A replication of learning-based PDE solvers with convergence guarantees as the target topic.
An R replication of least-cost modeling on irregular landscape graphs using graph and triangulation concepts.
A replication about depolarization block and sodium-channel inactivation in a model of midbrain dopamine neurons.
A replication concerning measures of contextual modulation in information transmission.
A replication of a thalamocortical network model whose subject is slow sleep oscillations and transitions between activity states.
A replication of habit formation as self-sustained sensorimotor patterns.
An R replication of insect phenology modeling with ordinal regression and continuation-ratio models.
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