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The mechanistic basis of data dependence and abrupt learning in an in-context classification task
In-context learning (ICL) is driven by the abrupt emergence of an induction head, which subsequently competes with in-weights learning.
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a "persistent prior" influence unique to DBM and absent from the other algorithms.
Receptor crosstalk improves concentration sensing of multiple ligands
Crosstalk is often thought of as a deviation from optimal specific recognition, as the binding of non-cognate ligands can interfere with the detection of the receptor's cognate ligand, possibly leading to a false triggering of a downstream signaling pathway.
Infomax strategies for an optimal balance between exploration and exploitation
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness.
