Optogenetics seminars

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. Touch is one sensory modality where temporal patterning carries key information, and the rodent whisker system is a prominent model for understanding neuronal coding and plasticity underlying touch sensation. Neurons in this system are precise encoders of fluctuations in whisker dynamics down to a timescale of milliseconds, but it is not clear whether they can refine their encoding abilities as a result of learning patterned stimuli. For example, can they enhance temporal integration to become better at distinguishing sequences? To explore how cortical coding plasticity underpins sequence discrimination, we developed a task in which mice distinguished between tactile ‘word’ sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory “barrel” cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal’s action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal’s learnt action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were already found in naïve mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf sequence learning results in neurons whose activity reflects the learnt association between target sequence and licking, rather than a refined representation of sensory features. Taken together with results from other laboratories, our findings suggest that neurons in sensory cortex are involved in task-specific processing and that an animal does not sense the world independently of what it needs to feel in order to guide behaviour.

My lab seeks to understand how the mammalian brain performs the computations that underlie cognitive functions, including decision-making, short-term memory, and spatial navigation, at the level of the building blocks of the nervous system, cell types and neural populations organized into circuits. We have developed methods to measure, manipulate, and analyze neural circuits across various spatial and temporal scales, including technology for virtual reality, optical imaging, optogenetics, intracellular electrophysiology, molecular sensors, and computational modeling. I will present recent work that uses large scale calcium imaging to reveal the functional organization of the mouse posterior cortex for flexible decision-making during spatial navigation in virtual reality. I will also discuss work that uses optogenetics and calcium imaging during a variety of decision-making tasks to highlight how cognitive experience and context greatly alter the cortical circuits necessary for navigation decisions.

In 1676, using candle light and a small glass sphere as the lens, van Leeuwenhoek discovered the microscopic world of living microorganisms. Today, using lasers, spatial light modulators, digital cameras and computers, we study the statistical and fluid mechanics of microswimmers in ways that were unimaginable only 50 years ago. With light we can image swimming bacteria in 3D, apply controllable force fields or sculpt their 3D environment. In addition to shaping the physical world outside cells we can use light to control the internal state of genetically modified bacteria. I will review our recent work with light-bacteria interactions, going from some fundamental problems in the fluid and statistical mechanics of microswimmers to the use of bacteria as propellers for micro-machines or as a "living" paint controlled by light.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

Understanding what drives foraging decisions in animals requires careful manipulation of the value of available options while monitoring animal choices. Value-based decision-making tasks, in combination with formal learning models, have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is even though fruit flies have served as a model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision-making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate a reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of value-based decisions.

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.

The design of neural circuits, with large numbers of neurons interconnected in vast networks, strongly suggest that they are specifically build to generate emergent functional properties (1). To explore this hypothesis, we have developed two-photon holographic methods to selective image and manipulate the activity of neuronal populations in 3D in vivo (2). Using them we find that groups of synchronous neurons (neuronal ensembles) dominate the evoked and spontaneous activity of mouse primary visual cortex (3). Ensembles can be optogenetically imprinted for several days and some of their neurons trigger the entire ensemble (4). By activating these pattern completion cells in ensembles involved in visual discrimination paradigms, we can bi-directionally alter behavioural choices (5). Our results demonstrate that ensembles are necessary and sufficient for visual perception and are consistent with the possibility that neuronal ensembles are the functional building blocks of cortical circuits. 1. R. Yuste, From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487-497 (2015). 2. L. Carrillo-Reid, W. Yang, J. E. Kang Miller, D. S. Peterka, R. Yuste, Imaging and Optically Manipulating Neuronal Ensembles. Annu Rev Biophys, 46: 271-293 (2017). 3. J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid, R. Yuste, Visual stimuli recruit intrinsically generated cortical ensembles. Proceedings of the National Academy of Sciences of the United States of America 111, E4053-4061 (2014). 4. L. Carrillo-Reid, W. Yang, Y. Bando, D. S. Peterka, R. Yuste, Imprinting and recalling cortical ensembles. Science 353, 691-694 (2016). 5. L. Carrillo-Reid, S. Han, W. Yang, A. Akrouh, R. Yuste, (2019). Controlling visually-guided behaviour by holographic recalling of cortical ensembles. Cell 178, 447-457. DOI:https://doi.org/10.1016/j.cell.2019.05.045.