Optogenetics seminars

Transient changes in dopamine activity in response to reward and punishment have been known to regulate reward-related learning. However, the cellular basis that detects the transient dopamine signaling has long been unclear. Using two-photon microscopy and optogenetics, I have shown that transient increases and decreases of dopamine modulate plasticity of dopamine D1 and D2 receptor-expressing cells in the nucleus accumbens, respectively. At the behavioral level, I characterized that these D1 and D2 cells cooperatively tune learning by generalization and discrimination learning. Interestingly, disturbance of the dopamine signaling impaired D2 cell plasticity and discrimination learning, which was analogous to salience misattribution seen in subjects with schizophrenia.

Animals constantly modify their behavior through experience. Flexible behavior is key to our ability to adapt to the ever-changing environment. My laboratory is interested in studying the activity of neuronal ensembles in behaving animals, and how it changes with learning. We have recently set up a paradigm where mice learn to associate sensory information (two different odors) to motor outputs (lick vs no-lick) under head-fixation. We combined this with two-photon calcium imaging, which can monitor the activity of a microcircuit of many tens of neurons simultaneously from a small area of the brain. Imaging the motor cortex during the learning of this task revealed neurons with diverse task-related response types. Intriguingly, different response types were spatially intermingled; even immediately adjacent neurons often had very different response types. As the mouse learned the task under the microscope, the activity coupling of neurons with similar response types specifically increased, even though they are intermingled with neurons with dissimilar response types. This suggests that intermingled subnetworks of functionally-related neurons form in a learning-related way, an observation that became possible with our cutting-edge technique combining imaging and behavior. We are working to extend this study. How plastic are neuronal microcircuits during other forms of learning? How plastic are they in other parts of the brain? What are the cellular and molecular mechanisms of the microcircuit plasticity? Are the observed activity and plasticity required for learning? How does the activity of identified individual neurons change over days to weeks? We are asking these questions, combining a variety of techniques including in vivo two-photon imaging, optogenetics, electrophysiology, genetics and behavior.

Over 60 years of research has established that medial temporal lobe structures, including the hippocampus and entorhinal cortex, are necessary for the formation of episodic memories (i.e. memories of specific personal events that occur in spatial and temporal context). While prior work to establish the neural mechanisms underlying episodic memory has largely focused on questions related spatial context, recently we have begun to investigate how these brain structures could be involved in encoding aspects of temporal context. In particular, we have focused on how medial entorhinal cortex, a structure well known for its role in spatial memory, may also be involved in encoding interval time. To answer this question we have developed an instrumental paradigm for head-fixed mice that requires both immobile interval timing and locomotion-dependent navigation behavior. By combining this behavioral paradigm with large-scale cellular resolution functional imaging and optogenetic-mediated inactivation, our results suggest that MEC is required for learning of interval timing behavior and that interval timing could be mediated through regular, sequential neural activity of a distinct subpopulation of neurons in MEC that encode elapsed time during periods of immobility (Heys and Dombeck, 2018; Heys et al, 2020; Issa et al., 2020). In this talk, I will discuss these findings and discuss our on-going work to investigate the principles underlying the role of medial temporal lobe structures in timing behavior and episodic memory.

Team work makes sensory streams work: our senses work together, learn from each other, and stand in for one another, the result of which is perception and understanding. Learned associations between stimuli in different sensory modalities can shape the way we perceive these stimuli (Mcgurk and Macdonald, 1976). During audio-visual associative learning, auditory cortex is thought to underlie multi-modal plasticity in visual cortex (McIntosh et al., 1998; Mishra et al., 2007; Zangenehpour and Zatorre, 2010). However, it is not well understood how processing in visual cortex is altered by an auditory stimulus that is predictive of a visual stimulus and what the mechanisms are that mediate such experience-dependent, audio-visual associations in sensory cortex. Here we describe a neural mechanism by which an auditory input can shape visual representations of behaviorally relevant stimuli through direct interactions between auditory and visual cortices. We show that the association of an auditory stimulus with a visual stimulus in a behaviorally relevant context leads to an experience-dependent suppression of visual responses in primary visual cortex (V1). Auditory cortex axons carry a mixture of auditory and retinotopically-matched visual input to V1, and optogenetic stimulation of these axons selectively suppresses V1 neurons responsive to the associated visual stimulus after, but not before, learning. Our results suggest that cross-modal associations can be stored in long-range cortical connections and that with learning these cross-modal connections function to suppress the responses to predictable input.

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.

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.

Seizure onset is a critically important brain state transition that has proved very difficult to predict accurately from recordings of brain activity. I will present new data acquired using a range of optogenetic and imaging tools to characterize exactly how cortical networks change in the build-up to a seizure. I will show how intermittent optogenetic stimulation ("active probing") reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. This data relates back to old work from the 1980s suggesting a critical role in epileptic pathophysiology for dendritic plateau potentials. Our data show how the precipitous nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms.

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 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.