Birthing a litter with several fathers may help when food is scarce. Breakthroughs, discoveries, and DIY tips sent six days a week. If a female house mouse mates with multiple male house mice, her litter could have multiple fathers. Polyandry, as this mating practice is called, is common for various species. Yet scientists are still investigating its purpose and the potential benefits of birthing half siblings within the same litter.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'Memory manipulation is inevitable': How rewriting memory in the lab might one day heal humans Professor and neuroscientist Steve Ramirez, shown working with brain samples, is exploring the science of memory manipulation. This is read by an automated voice. Please report any issues or inconsistencies here . Scientists have found that memories are not static records but dynamic processes that change the brain's wiring each time they are recalled.

Social conflict is a survival mechanism yielding both normal and pathological behaviors. To understand its underlying principles, we collected behavioral and whole-brain neural data from mice advancing through stages of social conflict. We modeled the animals' interactions as a normal-form game using Bayesian inference to account for the partial observability of animals' strengths. We find that our behavioral and neural data are consistent with the first-level Theory of Mind (1-ToM) model where mice form "primary" beliefs about the strengths of all mice involved and "secondary" beliefs that estimate the beliefs of their opponents. Our model identifies the brain regions that carry the information about these beliefs and offers a framework for studies of social behaviors in partially observable settings.

Understanding decision-making is a core goal in both neuroscience and psychology, and computational models have often been helpful in the pursuit of this goal. While many models have been developed for characterizing behavior in binary decision-making and bandit tasks, comparatively little work has focused on animal decision-making in more complex tasks, such as navigation through a maze. Inverse reinforcement learning (IRL) is a promising approach for understanding such behavior, as it aims to infer the unknown reward function of an agent from its observed trajectories through state space. However, IRL has yet to be widely applied in neuroscience. One potential reason for this is that existing IRL frameworks assume that an agent's reward function is fixed over time.

A core goal in systems neuroscience and neuroethology is to understand how neural circuits generate naturalistic behavior. One foundational idea is that complex naturalistic behavior may be composed of sequences of stereotyped behavioral syllables, which combine to generate rich sequences of actions. To investigate this, a common approach is to use autoregressive hidden Markov models (ARHMMs) to segment video into discrete behavioral syllables. While these approaches have been successful in extracting syllables that are interpretable, they fail to account for other forms of behavioral variability, such as differences in speed, which may be better described as continuous in nature. To overcome these limitations, we introduce a class of warped ARHMMs (WARHMM). As is the case in the ARHMM, behavior is modeled as a mixture of autoregressive dynamics.

This remains an elusive question in neuroscience. Whereas reinforcement learning often focuses on the design of algorithms that enable artificial agents to efficiently learn new tasks, here we develop a modeling framework to directly infer the empirical learning rules that animals use to acquire new behaviors.

Environment Animals Pets Dogs Pet dogs can help teens' mental health Breakthroughs, discoveries, and DIY tips sent every weekday. It's old news that having a dog provides a lot of benefits. Playing with a pooch can help our brains concentrate and relax, a family dog can help prevent food allergies in children, and even fulfill our primal need to nurture. They also may have some sway over some of the tiniest organisms around--the microbes that live in our bodies. A study published December 3 in the journal found that the family dog prompts changes in our gut microbiome that result in better mental health.

We thank the reviewers for their valuable feedback. We appreciate they recognize that the paper is " well-written " and " clear " (R#2, R#3, R#4, R#5), whose technical contribution " quality is solid " (R#2), " very good " (R#1, R#5) and " non-trivial " (R#3) while it considers " an important problem in ML " (R#3, R#4) which can " be of interest to many We hope to address all questions and concerns raised in the following. We disagree with the reviewer. We are actively working on applying it to the other application scenarios. E.g., sums (marginals) require only decomposability and smoothness, with the addition of determinism We agree that computations are simple, i.e., elegant, once the Note that our contribution is more theoretical than empirical.

We thank all three reviewers for their constructive and valuable feedback. They found our paper to be a "very interesting We do not have a final answer yet, but we will discuss some hypotheses in the final version. We will include these results in supplementary material. R1: Consider testing semantically relevant perturbations. Experimentally, mice allow for genetic tools for large scale recordings ( 8000 units).