Dataset Information

Using the pup retrieval instinct as reinforcement for efficient auditory learning in mice.

ABSTRACT: There is growing interest in the mechanisms for natural sensory learning in pro-social contexts. Studies using a maternal model of social behavior in the mouse have provided new insight into the auditory processing of behaviorally relevant pup vocalizations, which are used as communication signals to elicit pup retrieval behavior by adult females. Whether neural and behavioral plasticity in response to these vocalizations reflect auditory associative learning linking the sounds to pups, versus simply a change in maternal responsiveness to evolved vocal signals, remains an open question. Here we describe a T-maze paradigm to track auditory learning as we pair an initially neutral, non-ethological stimulus with delivery of a pup for retrieval, which is intrinsically reinforcing for rodents.•Training is rapid and completely appetitive.•Over a period of 7 × 50-minute daily training sessions, animals increasingly use the sound to guide their arm choice for pup retrieval, with an increase in performance from chance to an average of ~80% on day 7.•This pairing method establishes a newly-formed sensory association using a natural maternal behavioral response, and lays a solid foundation for studies into the neurochemical and circuit mechanisms that mediate auditory associative learning in natural social contexts.

SUBMITTER: Besosa CI

PROVIDER: S-EPMC7491147 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using the pup retrieval instinct as reinforcement for efficient auditory learning in mice.

Besosa Cristina I CI Dunlap Alex G AG Lu Kai K Liu Robert C RC

MethodsX 20200829

There is growing interest in the mechanisms for natural sensory learning in pro-social contexts. Studies using a maternal model of social behavior in the mouse have provided new insight into the auditory processing of behaviorally relevant pup vocalizations, which are used as communication signals to elicit pup retrieval behavior by adult females. Whether neural and behavioral plasticity in response to these vocalizations reflect auditory associative learning linking the sounds to pups, versus s ...[more]

PMID: 32983921

Similar Datasets

Project description:Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.

Dataset Information

Using the pup retrieval instinct as reinforcement for efficient auditory learning in mice.

Publications

Using the pup retrieval instinct as reinforcement for efficient auditory learning in mice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets