We introduce a random access parallel (RAP) imaging modality that uses a novel design inspired by a Newtonian telescope to image multiple spatially separated samples without moving parts or robotics. This scheme enables near simultaneous image capture of multiple petri dishes and random-access imaging with sub-millisecond switching times at the full resolution of the camera. This enables the RAP system to capture long duration records from different samples in parallel, which is not possible using conventional automated microscopes. The system is demonstrated by continuously imaging multiple cardiac monolayer and Caenorhabditis elegans (C. elegans) preparations.
All data generated during this study are included in the manuscript and supporting files
- Gil Bub
- Simon Thibault
Funders support enabled acquisition of equipment used in this study and also funded summer student stipends for undergraduate authors.
- Jonathan Ewbank, Aix Marseille Université, INSERM, CNRS, France
© 2021, Ashraf et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.
A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.