Concerted actions of distinct serotonin neurons orchestrate female pup care behavior.

In many mammalian species, female behavior towards infant conspecifics changes across reproductive stages. Sexually naïve females interact minimally or aggressively with infants, whereas the same animals exhibit extensive care behavior, even towards unrelated infants, after parturition. Here, we discovered that two distinct sets of serotonin neurons collectively mediate this dramatic transition in maternal behavior-serotonin neurons projecting to the medial preoptic area (mPOA) promote pup care in mothers, whereas those projecting to the bed nucleus of the stria terminalis (BNST) suppress pup interaction in virgin female mice.

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics.

Patch foraging is a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch value. Behavior could be explained by models integrating time and rewards antagonistically, scaled by a slowly varying latent patience state.

Concerted actions of distinct serotonin neurons orchestrate female pup care behavior.

In many mammalian species, female behavior towards infant conspecifics changes across reproductive stages. Sexually naïve females interact minimally or aggressively with infants, whereas the same animals exhibit extensive care behavior, even towards unrelated infants, after parturition. Here, we discovered that two distinct sets of serotonin neurons collectively mediate this dramatic transition in maternal behavior-serotonin neurons projecting to the medial preoptic area (mPOA) promote pup care in mothers, whereas those projecting to the bed nucleus of the stria terminalis (BNST) suppress pup interaction in virgin female mice.

A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion.

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations.

Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity.

A primary goal of systems neuroscience is to discover how ensembles of neurons transform inputs into goal-directed behavior, a process known as neural computation. A powerful framework for understanding neural computation uses neural dynamics - the rules that describe the temporal evolution of neural activity - to explain how goal-directed input-output transformations occur. As dynamical rules are not directly observable, we need computational models that can infer neural dynamics from recorded neural activity.

Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.

Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models.

Concerted modulation of spontaneous behavior and time-integrated whole-brain neuronal activity by serotonin receptors.

Serotonin neurons from the raphe nuclei project across the entire brain and modulate diverse physiology and behavior by acting on over a dozen receptors. Here, we took a step towards dissecting this complex process by examining the effects of agonists and antagonists of four widely expressed serotonin receptors (2A, 2C, 1A, and 1B) on spontaneous mouse behavior, which we related to time-integrated whole-brain neuronal activity as assessed by the expression of Fos, a canonical immediate-early gene product. Low-dimensional representations of behavioral and Fos map data revealed the dominant factors of variation in each domain, captured predictable differences across drug groups, and enabled predictions of behavioral changes following perturbations in Fos maps and vice versa.

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models.

Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process.

A Bayesian Hierarchical Model of Trial-To-Trial Fluctuations in Decision Criterion.

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations.

A line attractor encoding a persistent internal state requires neuropeptide signaling.

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant.

Encoding of female mating dynamics by a hypothalamic line attractor.

Females exhibit complex, dynamic behaviours during mating with variable sexual receptivity depending on hormonal status. However, how their brains encode the dynamics of mating and receptivity remains largely unknown. The ventromedial hypothalamus, ventrolateral subdivision contains oestrogen receptor type 1-positive neurons that control mating receptivity in female mice.

Causal evidence of a line attractor encoding an affective state.

Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position. In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Dynamical systems modelling has revealed that neurons in the hypothalamus exhibit approximate line-attractor dynamics in male mice during aggressive encounters.

Structured flexibility in recurrent neural networks via neuromodulation.

The goal of theoretical neuroscience is to develop models that help us better understand biological intelligence. Such models range broadly in complexity and biological detail. For example, task-optimized recurrent neural networks (RNNs) have generated hypotheses about how the brain may perform various computations, but these models typically assume a fixed weight matrix representing the synaptic connectivity between neurons.

Explaining dopamine through prediction errors and beyond.

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.

Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State.

Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting.

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics.

Patch foraging presents a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch value. Mouse behavior could be explained by a model integrating time and rewards antagonistically, scaled by a latent patience state.

Emergence of belief-like representations through reinforcement learning.

To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information.

Periodic hypothalamic attractor-like dynamics during the estrus cycle.

Cyclic changes in hormonal state are well-known to regulate mating behavior during the female reproductive cycle, but whether and how these changes affect the dynamics of neural activity in the female brain is largely unknown. The ventromedial hypothalamus, ventro-lateral subdivision (VMHvl) contains a subpopulation of VMHvl neurons that controls female sexual receptivity. Longitudinal single cell calcium imaging of these neurons across the estrus cycle revealed that overlapping but distinct subpopulations were active during proestrus (mating-accepting) vs.

Emergence of belief-like representations through reinforcement learning.

Unlabelled: To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules.

Spontaneous behaviour is structured by reinforcement without explicit reward.

Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward.

An approximate line attractor in the hypothalamus encodes an aggressive state.

The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA and VMHvl, respectively. Here, we perform dynamical systems modeling of population neuronal activity in these nuclei during social behaviors.

Imaging whole-brain activity to understand behavior.

The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits.

Mice exhibit stochastic and efficient action switching during probabilistic decision making.

In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a two-armed bandit task, thirsty mice use information about recent action and action–outcome histories to choose between two ports that deliver water probabilistically.