Neuroscience seminars

Dr. Looger will discuss reagents for tracking Ca2+, membrane potential ("voltage"), glutamate, GABA, acetylcholine, serotonin, dopamine, etc. He will also cover optogenetics tools and methods for correlative light/electron microscopy. They make all tools freely available to everyone and work to get them in the hands of people that have limited resources.

Remarkable advances in experimental neuroscience now enable us to simultaneously observe the activity of many neurons, thereby providing an opportunity to understand how the moment by moment collective dynamics of the brain instantiates learning and cognition. However, efficiently extracting such a conceptual understanding from large, high dimensional neural datasets requires concomitant advances in theoretically driven experimental design, data analysis, and neural circuit modeling. We will discuss how the modern frameworks of high dimensional statistics and deep learning can aid us in this process. In particular we will discuss: (1) how unsupervised tensor component analysis and time warping can extract unbiased and interpretable descriptions of how rapid single trial circuit dynamics change slowly over many trials to mediate learning; (2) how to tradeoff very different experimental resources, like numbers of recorded neurons and trials to accurately discover the structure of collective dynamics and information in the brain, even without spike sorting; (3) deep learning models that accurately capture the retina’s response to natural scenes as well as its internal structure and function; (4) algorithmic approaches for simplifying deep network models of perception; (5) optimality approaches to explain cell-type diversity in the first steps of vision in the retina.

The successful recognition of familiar persons is critical for social interactions. Despite extensive research on the neural representations of familiar faces, we know little about how such representations unfold as someone becomes familiar. In three EEG experiments, we elucidated how representations of face familiarity and identity emerge from different qualities of familiarization: brief perceptual exposure (Experiment 1), extensive media familiarization (Experiment 2) and real-life personal familiarization (Experiment 3). Time-resolved representational similarity analysis revealed that familiarization quality has a profound impact on representations of face familiarity: they were strongly visible after personal familiarization, weaker after media familiarization, and absent after perceptual familiarization. Across all experiments, we found no enhancement of face identity representation, suggesting that familiarity and identity representations emerge independently during face familiarization. Our results emphasize the importance of extensive, real-life familiarization for the emergence of robust face familiarity representations, constraining models of face perception and recognition memory.

New memories are initially labile and have to be consolidated into stable long-term representations. Current theories assume that this is supported by a shift in the neural substrate that supports the memory, away from rapidly plastic hippocampal networks towards more stable representations in the neocortex. Rehearsal, i.e. repeated activation of the neural circuits that store a memory, is thought to crucially contribute to the formation of neocortical long-term memory representations. This may either be achieved by repeated study during wakefulness or by a covert reactivation of memory traces during offline periods, such as quiet rest or sleep. My research investigates memory consolidation in the human brain with multivariate decoding of neural processing and non-invasive in-vivo imaging of microstructural plasticity. Using pattern classification on recordings of electrical brain activity, I show that we spontaneously reprocess memories during offline periods in both sleep and wakefulness, and that this reactivation benefits memory retention. In related work, we demonstrate that active rehearsal of learning material during wakefulness can facilitate rapid systems consolidation, leading to an immediate formation of lasting memory engrams in the neocortex. These representations satisfy general mnemonic criteria and cannot only be imaged with fMRI while memories are actively processed but can also be observed with diffusion-weighted imaging when the traces lie dormant. Importantly, sleep seems to hold a crucial role in stabilizing the changes in the contribution of memory systems initiated by rehearsal during wakefulness, indicating that online and offline reactivation might jointly contribute to forming long-term memories. Characterizing the covert processes that decide whether, and in which ways, our brains store new information is crucial to our understanding of memory formation. Directly imaging consolidation thus opens great opportunities for memory research.

Repeat-rich sequence blocks are considered major determinants for 3D folding and structural genome organization in the cell nucleus in all higher eukaryotes. Here, we discuss how megabase-scale chromatin domain and chromosomal compartment organization in adult mouse cerebral cortex is linked, in highly cell type-specific fashion, to multiple retrotransposon superfamilies which comprise the vast majority of mobile DNA elements in the murine genome. We show that neuronal megadomain architectures include an evolutionarily adaptive heterochromatic organization which, upon perturbation, unleashes proviruses from the Long Terminal Repeat (LTR) Endogenous Retrovirus family that exhibit strong tropism in mature neurons. Furthermore, we mapped, in the human brain, cell type-specific genomic integration patterns of the human pathogen and exogenous retrovirus, HIV, together with changes in genome organization and function of the HIV infected brain. Our work highlights the critical importance of chromosomal conformations and the ‘spatial genome’ for neuron- and glia-specific regulatory mechanisms and defenses aimed at exogenous and endogenous retrotransposons in the brain

Microglia are implicated in a variety of functions in the central nervous system, ranging from shaping neural circuits during early brain development, to surveying the brain parenchyma, and providing trophic support to neurons across the entire lifespan. In neurodegeneration, microglia have been considered for long time mere bystanders, accompanying and worsening neuronal damage. However, recent evidence indicates that microglia can causally contribute to neurodegenerative diseases, and that their dysfunction can even be at the origin of the pathology. In fact, the broad range of physiological roles microglia play in the healthy brain suggest that faulty microglia can initiate neurodegeneration through several possible mechanisms. In particular, in this seminar, we will discuss how dysfunctional microglia can affect synaptic function leading to pathological synapse loss, thus putting microglia center stage in the pathogenesis of brain disorders.

The cortical algorithm hypothesis states that there is one common computational framework to solve diverse cognitive problems such as vision, voice recognition and motion control. In my talk, I propose a strategy to guide the search for this algorithm and I present a few ideas on how some of its components might look like. I'll explain why a highly interdisciplinary approach is needed from neuroscience, computer science, mathematics and physics to make further progress in this important question.

In this lecture, I will present our recent progress on three aspects of population responses in the primary visual cortex: encoding local stimulus attributes, electrical microstimulation and higher visual function. In the first part I will focus on population encoding and reconstruction of contour shapes in V1 and the comparison between monkey and mouse visual responses. In the second part of the talk I will present the effects of microstimulation on neural population in V1 and the relation to evoked saccades. In the final part of the talk I will discuss top-down influences in V1 and their relation to higher visual functions.

Evolution has shaped neural circuits in a very specific manner, slowly and aimlessly incorporating computational innovations that increased the chances to survive and reproduce of the newly born species. The discoveries done by the Evolutionary Developmental (Evo-Devo) biology field during the last decades have been crucial for our understanding of the gradual emergence of such innovations. In turn, Computational Neuroscience practitioners modeling the brain are becoming increasingly aware of the need to build models that incorporate these innovations to replicate the computational strategies used by the brain to solve a given task. The goal of this workshop is to bring together experts from Systems and Computational Neuroscience, Machine Learning and the Evo-Devo field to discuss if and how knowing the evolutionary history of neural circuits can help us understand the way the brain works, as well as the relative importance of learned VS innate neural mechanisms.

Animals produce behavior by responding to a mixture of cues that arise both externally (sensory) and internally (neural dynamics and states). These cues are continuously produced and can be combined in different ways depending on the needs of the animal. However, the integration of these external and internal cues remains difficult to understand in natural behaviors. To address this gap, we have developed an unsupervised method to identify internal states from behavioral data, and have applied it to the study of a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using cues from their partner. This sensory-driven behavior dynamically modulates courtship directed at their partner. We use our unsupervised method to identify how the animal integrates sensory information into distinct underlying states. We then use this to identify the role of courtship neurons in either integrating incoming information or directing the production of the song, roles that were previously hidden. Our results reveal how animals compose behavior from previously unidentified internal states, a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity, and motor outputs.

Does that title put you to sleep or pique your interest? The goal of my presentation is to introduce a powerful yet under-utilized mathematical equation that is surprisingly effective at uncovering spatiotemporal patterns that are embedded in data -- but that might be inaccessible in traditional analysis methods due to low SNR or sparse spatial distribution. If you flunked calculus, then don't worry: the math is really easy, and I'll spend most of the time discussing intuition, simulations, and applications in real data. I will also spend some time in the beginning of the talk providing a bird's-eye-view of the empirical research in my lab, which focuses on mesoscale brain dynamics associated with error monitoring and response competition.

In addition to its role in the learning and expression of conditioned behavior, the amygdala has long been implicated in the regulation of persistent states, such as anxiety and drive. Yet, it is not evident what projections of the neuronal activity capture the functional role of the network across such different timescales, specifically when behavior and neuronal space are complex and high-dimensional. We applied a data-driven dynamical approach for the analysis of calcium imaging data from the basolateral amygdala, collected while mice performed complex, self-paced behaviors, including spatial exploration, free social interaction, and goal directed actions. The seemingly complex network dynamics was effectively described by a hierarchical, modular structure, that corresponded to behavior on multiple timescales. Our results describe the response of the network activity to perturbations along different dimensions and the interplay between slow, state-like representation and the fast processing of specific events and actions schemes. We suggest hierarchical dynamical models offer a unified framework to capture the involvement of the amygdala in transitions between persistent states underlying such different functions as sensory associative learning, action selection and emotional processing. * Work done in collaboration with Jan Gründemann, Sol Fustinana, Alejandro Tsai and Julien Courtin (@theLüthiLab)

While traumatic brain injury (TBI) acutely disrupts the cortex, most TBI-related disabilities reflect secondary injuries that accrue over time. The thalamus is a likely site of secondary damage because of its reciprocal connections with the cortex. Using a mouse model of mild cortical injury that does not directly damage subcortical structures (mTBI), we found a chronic increase in C1q expression specifically in the corticothalamic circuit. Increased C1q expression co-localized with neuron loss and chronic inflammation, and correlated with disruption in sleep spindles and emergence of epileptic activities. Blocking C1q counteracted these outcomes, suggesting that C1q is a disease modifier in mTBI. Single-nucleus RNA sequencing demonstrated that microglia are the source of thalamic C1q. Since the corticothalamic circuit is important for cognition and sleep, which can be impaired by TBI, this circuit could be a new target for treating TBI-related disabilities