A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.
Light-seeking strategies in Zebrafish larvae are dissected using a virtual-reality assay, and these data are used to establish minimal stochastic and neural-circuits models that quantitatively capture this behavior.
Experimental results in Drosophila support a model in which gene expression is fundamentally controlled by morphogens tuning the same transcription parameter for genes that are expressed in highly diverse patterns.
Teaching signals from "tutor" brain areas should be adapted to the plasticity mechanisms in "student" areas to achieve efficient learning in two-stage systems such as the vocal control circuit of the songbird.
Stochasticity introduced computationally into a gene expression oscillator creates heterogeneity in the time of differentiation of identical cells and offers robustness to the progenitor state and the outcome of cell division.
Models that generate tandem alignments of cell polarities are more readily compatible with the formation of PIN1 polarity patterns in plant leaf buds than the most widely accepted “up-the-gradient” model.
The evolving spatial distribution of nuclei between apical and basal surfaces of the developing retinal neuroepithelium is quantitatively described by a nonlinear diffusion equation accounting for crowding within the tissue.