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The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic dopamine observed in behaving animals. This Perspective helps to resolve some of the conflicting interpretations of dopamine that currently exist in the literature. We focus on the following three empirical challenges to the RPE theory of dopamine: why does dopamine (1) ramp up as animals approach rewards, (2) respond to sensory and motor features and (3) influence action selection? We argue that the prediction error concept, once it has been suitably modified and generalized based on an analysis of each computational problem, answers each challenge. Nonetheless, there are a number of additional empirical findings that appear to demand fundamentally different theoretical explanations beyond encoding RPE. Therefore, looking forward, we discuss the prospects for a unifying theory that respects the diversity of dopamine signaling and function as well as the complex circuitry that both underlies and responds to dopaminergic transmission.
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http://dx.doi.org/10.1038/s41593-024-01705-4 | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFJ Cataract Refract Surg
July 2025
Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China.
Purpose: To develop and validate a multimodal deep-learning model for predicting postoperative vault height and selecting implantable collamer lens (ICL) sizes using Anterior Segment Optical Coherence Tomography (AS-OCT) and Ultrasound Biomicroscope (UBM) images combined with clinical features.
Setting: West China Hospital of Sichuan University, China.
Design: Deep-learning study.
J Neurophysiol
September 2025
Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Repetition suppression, the reduced neural response upon repeated presentation of a stimulus, can be explained by models focussing on bottom-up (i.e. adaptation) or top-down (i.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Faculty of Science, Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals).
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations.
View Article and Find Full Text PDF