Parvalbumin-positive GABAergic interneurons (PV-INs) provide inhibitory control of excitatory neuron activity, coordinate circuit function, and regulate behavior and cognition. PV-INs are uniquely susceptible to loss and dysfunction in traumatic brain injury (TBI) and epilepsy but the cause of this susceptibility is unknown. One hypothesis is that PV-INs use specialized metabolic systems to support their high-frequency action potential firing and that metabolic stress disrupts these systems, leading to their dysfunction and loss. Metabolism-based therapies can restore PV-IN function after injury in preclinical TBI models. Based on these findings, we hypothesize that (1) PV-INs are highly metabolically specialized, (2) these specializations are lost after TBI, and (3) restoring PV-IN metabolic specializations can improve PV-IN function as well as TBI-related outcomes. Using novel single-cell approaches, we can now quantify cell-type-specific metabolism in complex tissues to determine whether PV-IN metabolic dysfunction contributes to the pathophysiology of TBI.

The rules governing behavior often vary with behavioral contexts. As a result, an action rewarded in one context may be discouraged in another. Animals and humans are capable of switching between behavioral strategies under different contexts and acting adaptively according to the variable rules, a flexibility that is thought to be mediated by the prefrontal cortex (PFC). However, how the PFC orchestrates the context-dependent switch of strategies remains unclear. Here we show that pathway-specific projection neurons in the medial PFC (mPFC) differentially contribute to context-instructed strategy selection. In mice trained in a decision-making task in which a previously established rule and a newly learned rule are associated with distinct contexts, the activity of mPFC neurons projecting to the dorsomedial striatum (mPFC-DMS) encodes the contexts and further represents decision strategies conforming to the old and new rules. Moreover, mPFC-DMS neuron activity is required for the context-instructed strategy selection. In contrast, the activity of mPFC neurons projecting to the ventral midline thalamus (mPFC-VMT) does not discriminate between the contexts, and represents the old rule even if mice have adopted the new one. Furthermore, these neurons act to prevent the strategy switch under the new rule. Our results suggest that mPFC-DMS neurons promote flexible strategy selection guided by contexts, whereas mPFC-VMT neurons favor fixed strategy selection by preserving old rules.

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation-based visual discrimination task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in decision tasks.

While it is generally accepted that neurons control complex behavior and brain computation, the role of non-neuronal cells in this context remains unclear. Astrocytes, glial cells of the central nervous system, exhibit complex forms of chemical excitation, most prominently calcium transients, evoked by local and projection neuron activity. In this talk, I will provide mechanistic links between astrocytes’ spatiotemporally complex activity patterns, neuronal molecular signaling, and behavior. Using a visual detection task, in vivo calcium imaging, robust statistical analyses, and machine learning approaches, my work shows that cortical astrocytes encode the animal's decision, reward, performance level, and sensory properties. Behavioral context and motor activity-related parameters strongly impact astrocyte responses. Error analysis confirms that astrocytes carry behaviorally relevant information, supporting astrocytes' complementary role to neuronal coding beyond their established homeostatic and metabolic roles.

Knowing where we are, where we have been, and where we are going is critical to many behaviors, including navigation and memory. One potential neuronal mechanism underlying this ability is phase precession, in which spatially tuned neurons represent sequences of positions by activating at progressively earlier phases of local network theta oscillations. Based on studies in rodents, researchers have hypothesized that phase precession may be a general neural pattern for representing sequential events for learning and memory. By recording human single-neuron activity during spatial navigation, we show that spatially tuned neurons in the human hippocampus and entorhinal cortex exhibit phase precession. Furthermore, beyond the neural representation of locations, we show evidence for phase precession related to specific goal states. Our find- ings thus extend theta phase precession to humans and suggest that this phenomenon has a broad func- tional role for the neural representation of both spatial and non-spatial information.

The ability to simultaneously record the activity from tens to thousands to tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics. As an example, we focus on the ability to execute learned actions in a reliable and stable manner. We hypothesize that the ability to perform a given behavior in a consistent manner requires that the latent dynamics underlying the behavior also be stable. The stable latent dynamics, once identified, allows for the prediction of various behavioral features, using models whose parameters remain fixed throughout long timespans. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.

The ability to simultaneously record the activity from tens to thousands and maybe even tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics, and argue that latent cortical dynamics within the manifold are the fundamental and stable building blocks of neural population activity.

Sensory experience and perceptual learning changes receptive field properties of cortical pyramidal neurons possibly mediated by long-term potentiation (LTP) of synapses. We have previously shown in the mouse somatosensory cortex (S1) that sensory-driven LTP in layer (L) 2/3 pyramidal neurons is dependent on higher order thalamic feedback from the posteromedial nucleus (POm), which is thought to convey contextual information from various cortical regions integrated with sensory input. We have followed up on this work by dissecting the cortical microcircuitry that underlies this form of LTP. We found that repeated pairing of Pom thalamocortical and intracortical pathway activity in brain slices induces NMDAr-dependent LTP of the L2/3 synapses that are driven by the intracortical pathway. Repeated pairing also recruits activity of vasoactive intestinal peptide (VIP) interneurons, whereas it reduces the activity of somatostatin (SST) interneurons. VIP interneuron-mediated inhibition of SST interneurons has been established as a motif for the disinhibition of pyramidal neurons. By chemogenetic interrogation we found that activation of this disinhibitory microcircuit motif by higher-order thalamic feedback is indispensable for eliciting LTP. Preliminary results in vivo suggest that VIP neuron activity also increases during sensory-evoked LTP. Together, this suggests that the higherorder thalamocortical feedback may help modifying the strength of synaptic circuits that process first-order sensory information in S1. To start characterizing the relationship between higher-order feedback and cortical plasticity during learning in vivo, we adapted a perceptual learning paradigm in which head-fixed mice have to discriminate two types of textures in order to obtain a reward. POm axons or L2/3 pyramidal neurons labeled with the genetically encoded calcium indicator GCaMP6s were imaged during the acquisition of this task as well as the subsequent learning of a new discrimination rule. We found that a subpopulation of the POm axons and L2/3 neurons dynamically represent textures. Moreover, upon a change in reward contingencies, a fraction of the L2/3 neurons re-tune their selectivity to the texture that is newly associated with the reward. Altogether, our data indicates that higher-order thalamic feedback can facilitate synaptic plasticity and may be implicated in dynamic sensory stimulus representations in S1, which depends on higher-order features that are associated with the stimuli.

My lab is focused on how brain encodes and modulates social interactions. Intraspecific social interactions are integral for survival and maintenance of society among all mammalian species. Despite the importance of social interactions, we lack a complete understanding of the brain circuitry involved in processing social behaviour. My lab investigates how the hypothalamic orexin (hypocretin) neurons and their downstream circuits participate in social interaction behaviours. These neurons are located exclusively in the hypothalamus that regulates complex and goal-directed behaviours. We recently identified that orexin neurons differentially encode interaction between familiar and novel animals. We are currently investigating how chronic social isolation, a risk factor for the development of social-anxiety like behaviours, affects orexin neuron activity and how we can manipulate the activity of these neurons to mitigate isolation-induced social deficits.

Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, which places limits on its ability to stabilize network activity [5]. Here we show that this slow response is inevitable in realistic neuronal morphologies. Furthermore, we reveal that global scaling can in fact be a source of instability unless responsiveness or scaling accuracy are sacrificed." "A neuron with tens of thousands of synapses must regulate its own excitability to compensate for changes in input. The time requirement for global feedback can introduce critical phase lags in a neuron’s response to perturbation. The severity of phase lag increases with neuron size. Further, a more expansive morphology worsens cell responsiveness and scaling accuracy, especially in distal regions of the neuron. Local pools of reserve receptors improve efficiency, potentiation, and scaling, but this comes at a cost. Trafficking large quantities of receptors requires time, exacerbating the phase lag and instability. Local homeostatic feedback mitigates instability, but this too comes at the cost of reducing scaling accuracy." "Realization of the phase lag instability requires a unified model of synaptic scaling, regulation, and transport. We present such a model with global and local feedback in realistic neuron morphologies (Fig. 1). This combined model shows that neurons face a tradeoff between stability, accuracy, and efficiency. Global feedback is required for synaptic scaling but favors either system stability or efficiency. Large receptor pools improve scaling accuracy in large morphologies but worsen both stability and efficiency. Local feedback improves the stability-efficiency tradeoff at the cost of scaling accuracy. This project introduces unexplored constraints on neuron size, morphology, and synaptic scaling that are weakened by an interplay between global and local feedback.

The vagus nerve contains a diversity of sensory neurons that detect peripheral stimuli such as blood pressure changes at the aortic arch, lung expansion during breathing, meal-induced stomach distension, and chemotherapeutics that induce nausea. Underlying vagal sensory mechanisms are largely unresolved at a molecular level, presenting tremendously important problems in sensory biology. We charted vagal sensory neurons by single cell RNA sequencing, identifying novel cell surface receptors and classifying a staggering diversity of sensory neuron types. We then generated a collection of ires-Cre knock-in mice to target each neuron type, and adapted genetic tools for Cre-based anatomical mapping, in vivo imaging, targeted ablation, and optogenetic control of vagal neuron activity. We found different sensory neuron types that innervate the lung and exert powerful effects on breathing, others that monitor and control the digestive system, and yet others that innervate that innervate the larynx and protect the airways. Together with Ardem Patapoutian, we also identified a critical role for Piezo mechanoreceptors in the sensation of airway stretch, which underlies a classical respiratory reflex termed the Hering-Breuer inspiratory reflex, as well as in the neuronal sensation of blood pressure and the baroreceptor reflex.