Cerebellar climbing fibers multiplex movement and reward signals during a voluntary movement task in mice.

Cerebellar climbing fibers convey sensorimotor information and their errors, which are used for motor control and learning. Furthermore, they represent reward-related information. Despite such functional diversity of climbing fiber signals, it is still unclear whether each climbing fiber conveys the information of single or multiple modalities and how the climbing fibers conveying different information are distributed over the cerebellar cortex. Here we perform two-photon calcium imaging from cerebellar Purkinje cells in mice engaged in a voluntary forelimb lever-pull task and demonstrate that climbing fiber responses in 68% of Purkinje cells can be explained by the combination of multiple behavioral variables such as lever movement, licking, and reward delivery. Neighboring Purkinje cells exhibit similar climbing fiber response properties, form functional clusters, and share noise fluctuations of responses. Taken together, individual climbing fibers convey behavioral information on multiplex variables and are spatially organized into the functional modules of the cerebellar cortex.

A reservoir of foraging decision variables in the mouse brain.

In any given situation, the environment can be parsed in different ways to yield decision variables (DVs) defining strategies useful for different tasks. It is generally presumed that the brain only computes a single DV defining the current behavioral strategy. Here to test this assumption, we recorded neural ensembles in the frontal cortex of mice performing a foraging task admitting multiple DVs. Methods developed to uncover the currently employed DV revealed the use of multiple strategies and occasional switches in strategy within sessions. Optogenetic manipulations showed that the secondary motor cortex (M2) is needed for mice to use the different DVs in the task. Surprisingly, we found that regardless of which DV best explained the current behavior, M2 activity concurrently encoded a full basis set of computations defining a reservoir of DVs appropriate for alternative tasks. This form of neural multiplexing may confer considerable advantages for learning and adaptive behavior.

Metastable attractors explain the variable timing of stable behavioral action sequences.

The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior.

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories. Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics. To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently. This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way. These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.

More than Just a "Motor": Recent Surprises from the Frontal Cortex.

Motor and premotor cortices are crucial for the control of movements. However, we still know little about how these areas contribute to higher-order motor control, such as deciding which movements to make and when to make them. Here we focus on rodent studies and review recent findings, which suggest that-in addition to motor control-neurons in motor cortices play a role in sensory integration, behavioral strategizing, working memory, and decision-making. We suggest that these seemingly disparate functions may subserve an evolutionarily conserved role in sensorimotor cognition and that further study of rodent motor cortices could make a major contribution to our understanding of the evolution and function of the mammalian frontal cortex.

An effect of serotonergic stimulation on learning rates for rewards apparent after long intertrial intervals.

Serotonin has widespread, but computationally obscure, modulatory effects on learning and cognition. Here, we studied the impact of optogenetic stimulation of dorsal raphe serotonin neurons in mice performing a non-stationary, reward-driven decision-making task. Animals showed two distinct choice strategies. Choices after short inter-trial-intervals (ITIs) depended only on the last trial outcome and followed a win-stay-lose-switch pattern. In contrast, choices after long ITIs reflected outcome history over multiple trials, as described by reinforcement learning models. We found that optogenetic stimulation during a trial significantly boosted the rate of learning that occurred due to the outcome of that trial, but these effects were only exhibited on choices after long ITIs. This suggests that serotonin neurons modulate reinforcement learning rates, and that this influence is masked by alternate, unaffected, decision mechanisms. These results provide insight into the role of serotonin in treating psychiatric disorders, particularly its modulation of neural plasticity and learning.

Distinct Sources of Deterministic and Stochastic Components of Action Timing Decisions in Rodent Frontal Cortex.

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals.

Preparing and selecting actions with neural populations: toward cortical circuit mechanisms.

How the brain selects one action among multiple alternatives is a central question of neuroscience. An influential model is that action preparation and selection arise from subthreshold activation of the very neurons encoding the action. Recent work, however, shows a much greater diversity of decision-related and action-related signals coexisting with other signals in populations of motor and parietal cortical neurons. We discuss how such distributed signals might be decoded by biologically plausible mechanisms. We also discuss how neurons within cortical circuits might interact with each other during action selection and preparation and how recurrent network models can help to reveal dynamical principles underlying cortical computation.

Activation of dorsal raphe serotonergic neurons promotes waiting but is not reinforcing.

BACKGROUND: The central neuromodulator serotonin (5-HT) has been implicated in a wide range of behaviors and affective disorders, but the principles underlying its function remain elusive. One influential line of research has implicated 5-HT in response inhibition and impulse control. Another has suggested a role in affective processing. However, whether and how these effects relate to each other is still unclear. RESULTS: Here, we report that optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DRN) produces a dose-dependent increase in mice's ability to withhold premature responding in a task that requires them to wait several seconds for a randomly delayed tone. The 5-HT effect had a rapid onset and was maintained throughout the stimulation period. In addition, movement speed was slowed, but photostimulation did not affect reaction time or time spent at the reward port. Using similar photostimulation protocols in place preference and value-based choice tests, we found no evidence of either appetitive or aversive effects of DRN 5-HT neuron activation. CONCLUSIONS: These results provide strong evidence that the efficacy of DRN 5-HT neurons in promoting waiting for delayed reward is independent of appetitive or aversive effects and support the importance of 5-HT in behavioral persistence and impulse control.

Neural antecedents of self-initiated actions in secondary motor cortex.

The neural origins of spontaneous or self-initiated actions are not well understood and their interpretation is controversial. To address these issues, we used a task in which rats decide when to abort waiting for a delayed tone. We recorded neurons in the secondary motor cortex (M2) and interpreted our findings in light of an integration-to-bound decision model. A first population of M2 neurons ramped to a constant threshold at rates proportional to waiting time, strongly resembling integrator output. A second population, which we propose provide input to the integrator, fired in sequences and showed trial-to-trial rate fluctuations correlated with waiting times. An integration model fit to these data also quantitatively predicted the observed inter-neuronal correlations. Together, these results reinforce the generality of the integration-to-bound model of decision-making. These models identify the initial intention to act as the moment of threshold crossing while explaining how antecedent subthreshold neural activity can influence an action without implying a decision.

Spontaneous Decisions and Free Will: Empirical Results and Philosophical Considerations.

Spontaneous actions are preceded by brain signals that may sometimes be detected hundreds of milliseconds in advance of a subject's conscious intention to act. These signals have been claimed to reflect prior unconscious decisions, raising doubts about the causal role of conscious will. Murakami et al. (2014. Nat Neurosci 17: 1574-1582) have recently argued for a different interpretation. During a task in which rats spontaneously decided when to abort waiting, the authors recorded neurons in the secondary motor cortex. The neural activity and relationship to action timing was parsimoniously explained using an integration-to-bound model, similar to those widely used to account for evidence-based decisions. In this model, the brain accumulates spontaneously occurring inputs voting for or against an action, but only commits to act once a certain threshold is crossed. The model explains how spontaneous decisions can be forecast (partially predicted) by neurons that reflect either the input or output of the integrator. It therefore presents an explicit hypothesis capable of rejecting the claim that such predictive signals imply unconscious decisions. We suggest that these results can inform the current debate on free will but must be considered with caution.

In vitro odor-aversion conditioning in a terrestrial mollusk.

We developed an in vitro odor-aversion conditioning system in the terrestrial mollusk, Limax, and found a behavioral correlate of network oscillation in the olfactory CNS. We first examined the odor-induced behavior of Limax, after odor-aversion conditioning in vivo. Shortening of mantle muscles was specifically observed in response to aversively conditioned odors. We previously identified that parietal nerves, which project to the mantle muscle in Limax, regulate shortening of the mantle muscle. We therefore isolated whole brains containing noses (sensory organs) and parietal nerves (motor output), and applied an odor-aversion conditioning paradigm to these in vitro preparations. Before the in vitro conditioning, application of attractive odors to the noses did not elicit any discharge in the parietal nerves. However, after odor-aversion conditioning, discharges in the parietal nerves were observed in response to the natively attractive but aversively conditioned odors. We also found that network oscillation frequency in the procerebrum (PC), the olfactory CNS of Limax, increased specifically in response to the aversively conditioned odors that elicited avoidance behavior. In naive (nonconditioned) preparations, increases in the PC oscillation frequency were observed specifically in response to innately aversive odors. These results indicate that the isolated brains have an ability of odor learning. They also suggest that changes in PC network oscillation are associated with aversively conditioned and innately aversive odors, both of which elicit avoidance behavior. This in vitro conditioning system would be an effective approach for exploring the neural mechanism to determine the aversion to odors.

State-dependent sensory gating in olfactory cortex.

Sensory systems show behavioral state-dependent gating of information flow that largely depends on the thalamus. Here we examined whether the state-dependent gating occurs in the central olfactory pathway that lacks a thalamic relay. In urethane-anesthetized rats, neocortical EEG showed a periodical alternation between two states: a slow-wave state (SWS) characterized by large and slow waves and a fast-wave state (FWS) characterized by faster waves. Single-unit recordings from olfactory cortex neurons showed robust spike responses to adequate odorants during FWS, whereas they showed only weak responses during SWS. The state-dependent change in odorant-evoked responses was observed in a majority of olfactory cortex neurons, but in only a small percentage of olfactory bulb neurons. These findings demonstrate a powerful state-dependent gating of odor information in the olfactory cortex that works in synchrony with the gating of other sensory systems. They suggest a state-dependent switchover of signal processing modes in the olfactory cortex.

Odor-evoked responses in the olfactory center neurons in the terrestrial slug.

The procerebrum (PC) of the terrestrial mollusk Limax is a highly developed second-order olfactory center consisting of two electrophysiologically distinct populations of neurons: nonbursting (NB) and bursting (B). NB neurons are by far the more numerous of the two cell types. They receive direct synaptic inputs from afferent fibers from the tentacle ganglion, the primary olfactory center, and also receive periodic inhibitory postsynaptic potentials (IPSPs) from B neurons. Odor-evoked activity in the NB neurons was examined using perforated patch recordings. Stimulation of the superior tentacle with odorants resulted in inhibitory responses in 45% of NB neurons, while 11% of NB neurons showed an excitatory response. The specific response was reproducible in each neuron to the same odorant, suggesting the possibility that activity of NB neurons may encode odor identity. Analysis of the cycle-averaged membrane potential of NB neurons revealed a correlation between the firing rate and the membrane potential at the plateau phase between IPSPs. Also, the firing rate of NB neurons was affected by the frequency of the IPSPs. These results indicate the existence of two distinct mechanisms for the regulation of NB neuron activity.