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Low intensity rTMS: age dependent effects, and mechanisms underlying neural plasticity
Neuroplasticity is essential for the establishment and strengthening of neural circuits. Repetitive transcranial magnetic stimulation (rTMS) is commonly used to modulate cortical excitability and shows promise in the treatment of some neurological disorders. Low intensity magnetic stimulation (LI-rTMS), which does not directly elicit action potentials in the stimulated neurons, have also shown some therapeutic effects, and it is important to determine the biological mechanisms underlying the effects of these low intensity magnetic fields, such as would occur in the regions surrounding the central high-intensity focus of rTMS. Our team has used a focal low-intensity (10mT) magnetic stimulation approach to address some of these questions and to identify cellular mechanisms. I will present several studies from our laboratory, addressing (1) effects of LIrTMS on neuronal activity and excitability ; and (2) neuronal morphology and post-lesion repair. The ensemble of our results indicate that the effects of LI-rTMS depend upon the stimulation pattern, the age of the animal, and the presence of cellular magnetoreceptors.
Stability of visual processing in passive and active vision
The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.
Prefrontal mechanisms involved in learning distractor-resistant working memory in a dual task
Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.
Effect of nutrient sensing by microglia on mouse behavior
Microglia are the brain macrophages, eliciting multifaceted functions to maintain brain homeostasis across lifetime. To achieve this, microglia are able to sense a plethora of signals in their close environment. In the lab, we investigate the effect of nutrients on microglia function for several reasons: 1) Microglia express all the cellular machinery required to sense nutrients; 2) Eating habits have changed considerably over the last century, towards diets rich in fats and sugars; 3) This so-called "Western diet" is accompanied by an increase in the occurrence of neuropathologies, in which microglia are known to play a role. In my talk, I will present data showing how variations in nutrient intake alter microglia function, including exacerbation of synaptic pruning, with profound consequences for neuronal activity and behavior. I will also show unpublished data on the mechanisms underlying the effects of nutrients on microglia, notably through the regulation of their metabolic activity.
Metabolic Remodelling in the Developing Forebrain in Health and Disease
Little is known about the critical metabolic changes that neural cells have to undergo during development and how temporary shifts in this program can influence brain circuitries and behavior. Motivated by the identification of autism-associated mutations in SLC7A5, a transporter for metabolically essential large neutral amino acids (LNAAs), we utilized metabolomic profiling to investigate the metabolic states of the cerebral cortex across various developmental stages. Our findings reveal significant metabolic restructuring occurring in the forebrain throughout development, with specific groups of metabolites exhibiting stage-specific changes. Through the manipulation of Slc7a5 expression in neural cells, we discovered an interconnected relationship between the metabolism of LNAAs and lipids within the cortex. Neuronal deletion of Slc7a5 influences the postnatal metabolic state, resulting in a shift in lipid metabolism and a cell-type-specific modification in neuronal activity patterns. This ultimately gives rise to enduring circuit dysfunction.
In vivo direct imaging of neuronal activity at high temporospatial resolution
Advanced noninvasive neuroimaging methods provide valuable information on the brain function, but they have obvious pros and cons in terms of temporal and spatial resolution. Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) effect provides good spatial resolution in the order of millimeters, but has a poor temporal resolution in the order of seconds due to slow hemodynamic responses to neuronal activation, providing indirect information on neuronal activity. In contrast, electroencephalography (EEG) and magnetoencephalography (MEG) provide excellent temporal resolution in the millisecond range, but spatial information is limited to centimeter scales. Therefore, there has been a longstanding demand for noninvasive brain imaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. In this talk, I will introduce a novel approach that enables Direct Imaging of Neuronal Activity (DIANA) using MRI that can dynamically image neuronal spiking activity in milliseconds precision, achieved by data acquisition scheme of rapid 2D line scan synchronized with periodically applied functional stimuli. DIANA was demonstrated through in vivo mouse brain imaging on a 9.4T animal scanner during electrical whisker-pad stimulation. DIANA with milliseconds temporal resolution had high correlations with neuronal spike activities, which could also be applied in capturing the sequential propagation of neuronal activity along the thalamocortical pathway of brain networks. In terms of the contrast mechanism, DIANA was almost unaffected by hemodynamic responses, but was subject to changes in membrane potential-associated tissue relaxation times such as T2 relaxation time. DIANA is expected to break new ground in brain science by providing an in-depth understanding of the hierarchical functional organization of the brain, including the spatiotemporal dynamics of neural networks.
The Geometry of Decision-Making
Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. Despite this most studies have considered only on the outcome of, and time taken to make, decisions. Motion is, however, crucial in terms of how space is represented by organisms during spatial decision-making. Employing a range of new technologies, including automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) for animals, experiments with fruit flies, locusts and zebrafish (representing aerial, terrestrial and aquatic locomotion, respectively), I will demonstrate that this time-varying representation results in the emergence of new and fundamental geometric principles that considerably impact decision-making. Specifically, we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (‘critical’) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. Due to the critical nature of these transitions (and the corresponding increase in ‘susceptibility’) even noisy brains are extremely sensitive to very small differences between remaining options (e.g., a very small difference in neuronal activity being in “favor” of one option) near these locations in space-time. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.
Meta-learning functional plasticity rules in neural networks
Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will present how to scale-up this approach to recurrent spiking networks using simulation-based inference.
Prefrontal top-down projections control context-dependent strategy selection
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.
Restructuring cortical feedback circuits
We hardly notice when there is a speck on our glasses, the obstructed visual information seems to be magically filled in. The mechanistic basis for this fundamental perceptual phenomenon has, however, remained obscure. What enables neurons in the visual system to respond to context when the stimulus is not available? While feedforward information drives the activity in cortex, feedback information is thought to provide contextual signals that are merely modulatory. We have made the discovery that mouse primary visual cortical neurons are strongly driven by feedback projections from higher visual areas when their feedforward sensory input from the retina is missing. This drive is so strong that it makes visual cortical neurons fire as much as if they were receiving a direct sensory input. These signals are likely used to predict input from the feedforward pathway. Preliminary results show that these feedback projections are strongly influenced by experience and learning.
From a by-stander to an influencer: How microglia adapt to altered environments and influence neuronal activity
Microglia, traditionally classified as immune-responsive, adjust synaptic connections during development and disease. However, their role in the adult nervous system has been mostly diminished to an observer. In my research group, we are interested in how microglia are involved in establishing and maintaining accurate neuronal circuit function in the retina and in the visual cortex. In my talk, I will introduce our strategies how to decipher the microglia’s functional identity and how this information guided us to microglia enabled extracellular matrix remodeling and reinstatment of juvenile-like plasticity in the adult brain.
Mesmerize: A blueprint for shareable and reproducible analysis of calcium imaging data
Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.
NMC4 Short Talk: Different hypotheses on the role of the PFC in solving simple cognitive tasks
Low-dimensional population dynamics can be observed in neural activity recorded from the prefrontal cortex (PFC) of subjects performing simple cognitive tasks. Many studies have shown that recurrent neural networks (RNNs) trained on the same tasks can reproduce qualitatively these state space trajectories, and have used them as models of how neuronal dynamics implement task computations. The PFC is also viewed as a conductor that organizes the communication between cortical areas and provides contextual information. It is then not clear what is its role in solving simple cognitive tasks. Do the low-dimensional trajectories observed in the PFC really correspond to the computations that it performs? Or do they indirectly reflect the computations occurring within the cortical areas projecting to the PFC? To address these questions, we modelled cortical areas with a modular RNN and equipped it with a PFC-like cognitive system. When trained on cognitive tasks, this multi-system brain model can reproduce the low-dimensional population responses observed in neuronal activity as well as classical RNNs. Qualitatively different mechanisms can emerge from the training process when varying some details of the architecture such as the time constants. In particular, there is one class of models where it is the dynamics of the cognitive system that is implementing the task computations, and another where the cognitive system is only necessary to provide contextual information about the task rule as task performance is not impaired when preventing the system from accessing the task inputs. These constitute two different hypotheses about the causal role of the PFC in solving simple cognitive tasks, which could motivate further experiments on the brain.
Generative models of brain function: Inference, networks, and mechanisms
This talk will focus on the generative modelling of resting state time series or endogenous neuronal activity. I will survey developments in modelling distributed neuronal fluctuations – spectral dynamic causal modelling (DCM) for functional MRI – and how this modelling rests upon functional connectivity. The dynamics of brain connectivity has recently attracted a lot of attention among brain mappers. I will also show a novel method to identify dynamic effective connectivity using spectral DCM. Further, I will summarise the development of the next generation of DCMs towards large-scale, whole-brain schemes which are computationally inexpensive, to the other extreme of the development using more sophisticated and biophysically detailed generative models based on the canonical microcircuits.
Activity dependent myelination: a mechanism for learning and regeneration?
The CNS is responsive to an ever-changing environment. Until recently, studies of neural plasticity focused almost exclusively on functional and structural changes of neuronal synapses. In recent years, myelin plasticity has emerged as a potential modulator of neural networks. Myelination of previously unmyelinated axons, and changes in the structure on already-myelinated axons, can have large effects on network function. The heterogeneity of the extent of how axons in the CNS are myelinated offers diverse scope for dynamic myelin changes to fine-tune neural circuits. The traditionally held view of myelin as a passive insulator of axons is now changing to one of lifelong changes in myelin, modulated by neuronal activity and experience. Myelin, produced by oligodendrocytes (OLs), is essential for normal brain function, as it provides fast signal transmission, promotes synchronization of neuronal signals and helps to maintain neuronal function. OLs differentiate from oligodendrocyte precursor cells (OPCs), which are distributed throughout the adult brain, and myelination continues into late adulthood. OPCs can sense neuronal activity as they receive synaptic inputs from neurons and express voltage-gated ion channels and neurotransmitter receptors, and differentiate into myelinating OLs in response to changes in neuronal activity. This lecture will explore to what extent myelin plasticity occurs in adult animals, whether myelin changes occur in non-motor learning tasks, especially in learning and memory, and questions whether myelin plasticity and myelin regeneration are two sides of the same coin.
Large-scale approaches for distributed circuits underlying visual decision-making
Mammalian vision and visually-guided behavior relies on neurons distributed across diverse brain regions. In this talk I will describe our efforts to create tools that allow us to measure activity from these distributed circuits - Neuropixels probes for large-scale electrophysiology - and our findings from studies deploying these tools to study visual detection and discrimination in mice.
Multiphoton imaging with next-generation indicators
Two-photon (2P) in vivo functional imaging of genetically encoded fluorescent Ca2+indicators (GECIs) for neuronal activity has become a broadly applied standard tool in modern neuroscience, because it allows simultaneous imaging of the activity of many neurons at high spatial resolution within living animals. Unfortunately, the most commonly used light-sources – tunable femtosecond pulsed ti:sapphire lasers – can be prohibitively expensive for many labs and fall short of delivering sufficient powers for some new ultra-fast 2P microscopy modalities. Inexpensive homebuilt or industrial light sources such as Ytterbium fiber lasers (YbFLs) show great promise to overcome these limitations as they are becoming widely available at costs orders of magnitude lower and power outputs of up to many times higher than conventional ti:sapphire lasers. However, these lasers are typically bound to emitting a single wavelength (i.e., not tunable) centered around 1020-1060 nm, which fails to efficiently excite state of the art green GECIs such as jGCaMP7 or 8. To this end, we designed and characterized spectral variants (yellow CaMP = YCaMP) of the ultrasensitive genetically encoded calcium indicator jGCaMP7, that allows for efficient 2P-excitation at wavelengths above 1010nm. In this talk I will give a brief overview over some of the reasons why using a fiber laser for 2P excitation might be right for you. I will talk about the development of jYCaMP and some exciting new experimental avenues that it has opened while touching on the prospect that shifting biosensors yellow could have for the 2P imaging community. Please join me for an interesting and fun discussion on whether “yellow is the new green” after the talk!
Estimation of current and future physiological states in insular cortex
Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. While human insular cortex (InsCtx) is implicated in interoception, the cellular and circuit mechanisms remain unclear. I will describe our recent work imaging mouse InsCtx neurons during two physiological deficiency states – hunger and thirst. InsCtx ongoing activity patterns reliably tracked the gradual return to homeostasis, but not changes in behavior. Accordingly, while artificial induction of hunger/thirst in sated mice via activation of specific hypothalamic neurons (AgRP/SFOGLUT) restored cue-evoked food/water-seeking, InsCtx ongoing activity continued to reflect physiological satiety. During natural hunger/thirst, food/water cues rapidly and transiently shifted InsCtx population activity to the future satiety-related pattern. During artificial hunger/thirst, food/water cues further shifted activity beyond the current satiety-related pattern. Together with circuit-mapping experiments, these findings suggest that InsCtx integrates visceral-sensory inputs regarding current physiological state with hypothalamus-gated amygdala inputs signaling upcoming ingestion of food/water, to compute a prediction of future physiological state.
Measuring behavior to measure the brain
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.
Structures in space and time - Hierarchical network dynamics in the amygdala
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)
Non-Telecentric 2P microscopy for 3D random access mesoscale imaging
Ultra-low-cost, easily implemented and flexible two-photon scanning microscopy modification offering a several-fold expanded three-dimensional field of view that also maintains single-cell resolution. Application of our system for imaging neuronal activity has been demonstrated on mice, zebrafish and fruit flies. Website: https://github.com/BadenLab/nTCscope
Astrocytes contribute to remote memory formation by modulating hippocampal-cortical communication during learning
How is it that some memories fade in a day while others last forever? The formation of long-lasting (remote) memories depends on the coordinated activity between the hippocampus and frontal cortices, but the timeline of these interactions is debated. Astrocytes, star-shaped glial cells, sense and modify neuronal activity, but their role in remote memory is scarcely explored. We manipulated the activity of hippocampal astrocytes during memory acquisition and discovered it impaired remote, but not recent, memory retrieval. We also revealed a massive recruitment of cortical-projecting hippocampal neurons during memory acquisition, a process that is specifically inhibited by astrocytic manipulation. Finally, we directly inhibited this projection during memory acquisition to prove its necessity for the formation of remote memory. Our findings reveal that the foundation of remote memory can be established during acquisition with projection-specific effect of astrocytes.
Robust Encoding of Abstract Rules by Distinct Neuronal Populations in Primate Visual Cortex
I will discuss our recent evidence showing that information about abstract rules can be decoded from neuronal activity in primate visual cortex even in the absence of sensory stimulation. Furthermore, that rule information is greatest among neurons with the least visual activity and the weakest coupling to local neuronal networks. In addition, I will talk about recent developments in large-scale neurophysiological techniques in nonhuman primates.
The role of orexin/hypocretin in social behaviour
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.
Values Encoded in Orbitofrontal Cortex Are Causally Linked to Economic Choices
Classic economists proposed that economic choices rely on the computation and comparison of subjective values. This hypothesis continues to inform economic theory and experimental research, but behavioral measures are ultimately not sufficient to prove the proposal. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods. Moreover, neuronal activity in this area suggests the formation of a decision. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking choices to value signals in the brain remains correlational. In my talk, I will present recent results showing that neuronal activity in OFC are causal to economic choices.
Tools for Analyzing and Repairing the Brain. (Simultaneous translation to Spanish)
To enable the understanding and repair of complex biological systems, such as the brain, we are creating novel optical tools that enable molecular-resolution maps of such systems, as well as technologies for observing and controlling high-speed physiological dynamics in such systems. First, we have developed a method for imaging specimens with nanoscale precision, by embedding them in a swellable polymer, homogenizing their mechanical properties, and exposing them to water – which causes them to expand manyfold isotropically. This method, which we call expansion microscopy (ExM), enables ordinary microscopes to do nanoscale imaging, in a multiplexed fashion – important, for example, for brain mapping. Second, we have developed a set of genetically-encoded reagents, known as optogenetic tools, that when expressed in specific neurons, enable their electrical activities to be precisely driven or silenced in response to millisecond timescale pulses of light. Finally, we are designing, and evolving, novel reagents, such as fluorescent voltage indicators and somatically targeted calcium indicators, to enable the imaging of fast physiological processes in 3-D with millisecond precision. In this way we aim to enable the systematic mapping, control, and dynamical observation of complex biological systems like the brain. The talk will be simultaneously interpreted English-Spanish) by the Interpreter, Mg. Lourdes Martino. Para permitir la comprensión y reparación de sistemas biológicos complejos, como el cerebro, estamos creando herramientas ópticas novedosas que permiten crear mapas de resolución molecular de dichos sistemas, así como tecnologías para observar y controlar la dinámica fisiológica de alta velocidad en dichos sistemas. Primero, hemos desarrollado un método para obtener imágenes de muestras con precisión a nanoescala, incrustándolas en un polímero hinchable, homogeneizando sus propiedades mecánicas y exponiéndolas al agua, lo que hace que se expandan muchas veces isotrópicamente. Este método, que llamamos microscopía de expansión (ExM), permite que los microscopios ordinarios obtengan imágenes a nanoescala, de forma multiplexada, lo que es importante, por ejemplo, para el mapeo cerebral. En segundo lugar, hemos desarrollado un conjunto de reactivos codificados genéticamente, conocidos como herramientas optogenéticas, que cuando se expresan en neuronas específicas, permiten que sus actividades eléctricas sean activadas o silenciadas con precisión en respuesta a pulsos de luz en una escala de tiempo de milisegundos. Finalmente, estamos diseñando y desarrollando reactivos novedosos, como indicadores de voltaje fluorescentes e indicadores de calcio dirigidos somáticamente, para permitir la obtención de imágenes de procesos fisiológicos rápidos en 3-D con precisión de milisegundos. De esta manera, nuestro objetivo es permitir el mapeo sistemático, el control y la observación dinámica de sistemas biológicos complejos como el cerebro. La conferencia será traducida simultáneamente al español por la intérprete Mg. Lourdes Martino.