Neuroscience seminars

Olfactory navigation provides a tractable model for studying the circuit basis of sensori-motor transformations and goal-directed behaviour. Macroscopic organisms typically navigate in odor plumes that provide a noisy and uncertain signal about the location of an odor source. Work in many species has suggested that animals accomplish this task by combining temporal processing of dynamic odor information with an estimate of wind direction. Our lab has been using adult walking Drosophila to understand both the computational algorithms and the neural circuits that support navigation in a plume of attractive food odor. We developed a high-throughput paradigm to study behavioural responses to temporally-controlled odor and wind stimuli. Using this paradigm we found that flies respond to a food odor (apple cider vinegar) with two behaviours: during the odor they run upwind, while after odor loss they perform a local search. A simple computational model based one these two responses is sufficient to replicate many aspects of fly behaviour in a natural turbulent plume. In on-going work, we are seeking to identify the neural circuits and biophysical mechanisms that perform the computations delineated by our model. Using electrophysiology, we have identified mechanosensory neurons that compute wind direction from movements of the two antennae and central mechanosensory neurons that encode wind direction are are involved in generating a stable downwind orientation. Using optogenetic activation, we have traced olfactory circuits capable of evoking upwind orientation and offset search from the periphery, through the mushroom body and lateral horn, to the central complex. Finally, we have used optogenetic activation, in combination with molecular manipulation of specific synapses, to localize temporal computations performed on the odor signal to olfactory transduction and transmission at specific synapses. Our work illustrates how the tools available in fruit fly can be applied to dissect the mechanisms underlying a complex goal-directed behaviour.

The remarkable cognitive abilities characterizing humans has been linked to unique patterns of connectivity characterizing the neocortex. Comparative studies have shown that human cortical pyramidal neurons (PN) receive a significant increase of synaptic inputs when compared to other mammals, including non-human primates and rodents, but how this may relate to changes in cortical connectivity and function remained largely unknown. We previously identified a human-specific gene duplication (HSGD), SRGAP2C, that, when induced in mouse cortical PNs drives human-specific features of synaptic development, including a correlated increase in excitatory (E) and inhibitory (I) synapse density through inhibition of the ancestral SRGAP2A protein (Charrier et al. 2012; Fossatti et al. 2016; Schmidt et al. 2019). However, the origin and nature of this increased connectivity and its impact on cortical circuit function was unknown. I will present new results exploring these questions (see Schmidt et al. (2020) https://www.biorxiv.org/content/10.1101/852970v1). Using a combination of transgenic approaches and quantitative monosynaptic tracing, we discovered that humanization of SRGAP2C expression in the mouse cortex leads to a specific increase in local and long-range cortico-cortical inputs received by layer 2/3 cortical PNs. Moreover, using in vivo two-photon imaging in the barrel cortex of awake mice, we show that humanization of SRGAP2C expression increases the reliability and selectivity of sensory- evoked responses in layer 2/3 PNs. We also found that mice humanized for SRGAP2C in all cortical pyramidal neurons and throughout development are characterized by improved behavioural performance in a novel whisker-based sensory discrimination task compared to control wild-type mice. Our results suggest that the emergence of SRGAP2C during human evolution underlie a new substrate for human brain evolution whereby it led to increased local and long-range cortico-cortical connectivity and improved reliability of sensory-evoked cortical coding. References cited Charrier C.*, Joshi K. *, Coutinho-Budd J., Kim, J-E., Lambert N., de Marchena, J., Jin W-L., Vanderhaeghen P., Ghosh A., Sassa T, and Polleux F. (2012) Inhibition of SRGAP2 function by its human-specific paralogs induces neoteny of spine maturation. Cell 149:923-935. * Co-first authors. Fossati M, Pizzarelli R, Schmidt ER, Kupferman JV, Stroebel D, Polleux F*, Charrier C*. (2016) SRGAP2 and Its Human-Specific Paralog Co-Regulate the Development of Excitatory and Inhibitory Synapses. Neuron. 91(2):356-69. * Co-senior corresponding authors. Schmidt E.R.E., Kupferman J.V., Stackmann M., Polleux F. (2019) The human-specific paralogs SRGAP2 and SRGAP2C differentially modulate SRGAP2A-dependent synaptic development. Scientific Rep. 9(1):18692. Schmidt E.R.E, Zhao H.T., Hillman E.M.C., Polleux F. (2020) Humanization of SRGAP2C expression increases cortico-cortical connectivity and reliability of sensory-evoked responses in mouse brain. Submitted. See also: https://www.biorxiv.org/content/10.1101/852970v1

This talk aims to show how new physical methods can advance biological and biomedical research. A major advance in physical chemistry in the last two decades has been the development of quantitative methods to directly observe individual molecules in solution, attached to surfaces, in the membrane of live cells or more recently inside live cells. These single-molecule fluorescence studies have now reached a stage where they can provide new insights into important biological problems. After presenting the principles of these methods, I will give some examples from our current research to probe the molecular basis of neurodegeneration. Here we have used single-molecule fluorescence to detect and analyse the low concentrations of soluble protein aggregates thought to be responsible for Alzheimer’s disease and determine the mechanisms by which they damage neurons. Lastly, I will describe how fundamental science aimed at watching single molecules incorporating nucleotides into DNA gave rise to a new rapid method to sequence DNA that is now widely used.

The dynamic nature of sleep in most animals suggests distinct stages which serve different functions. Genetic sleep induction methods in animal models provide a powerful way to disambiguate these stages and functions, although behavioural methods alone are insufficient to accurately identify what kind of sleep is being engaged. In Drosophila, activation of the dorsal fan-shaped body (dFB) promotes sleep, but it remains unclear what kind of sleep this is, how the rest of the fly brain is behaving, or if any specific sleep functions are being achieved. Here, we developed a method to record calcium activity from thousands of neurons across a volume of the fly brain during dFB-induced sleep, and we compared this to the effects of a sleep-promoting drug. We found that drug-induced spontaneous sleep decreased brain activity and connectivity, whereas dFB sleep was not different from wakefulness. Paradoxically, dFB-induced sleep was found to be even deeper than drug- induced sleep. When we probed the sleeping fly brain with salient visual stimuli, we found that the activity of visually-responsive neurons was blocked by dFB activation, confirming a disconnect from the external environment. Prolonged optogenetic dFB activation nevertheless achieved a significant sleep function, by correcting visual attention defects brought on by sleep deprivation. These results suggest that dFB activation promotes a distinct form of sleep in Drosophila, where brain activity and connectivity remain similar to wakefulness, but responsiveness to external sensory stimuli is profoundly suppressed.

Animals perform learned actions with remarkable consistency for years after acquiring a skill. What is the neural correlate of this stability? We explore this question from the perspective of neural populations. Recent work suggests that the building blocks of neural function may be the activation of population-wide activity patterns: neural modes that capture the dominant co-variation patterns of population activity and define a task specific low dimensional neural manifold. The time-dependent activation of the neural modes results in latent dynamics. We hypothesize that the latent dynamics associated with the consistent execution of a behaviour need to remain stable, and use an alignment method to establish this stability. Once identified, stable latent dynamics allow for the prediction of various behavioural features via fixed decoder models. We conclude that latent cortical dynamics within the task manifold are the fundamental and stable building blocks underlying consistent behaviour.

Our laboratory seeks to understand how neural circuits receive, compute, encode and transmit information. More specifically, we’d like to learn what biophysical and morphological features equip synapses, neurons and networks to perform these tasks. The retina is a model system for the study of neuronal information processing: We can deliver precisely defined physiological stimuli and record responses from many different cell types at various points within the network; in addition, retinal circuitry is particularly well understood, enabling us to interpret more directly the impact of synaptic and cellular mechanisms on circuit function. I will present recent experiments in the lab that exploit these advantages to examine how synapses and neurons within retinal amacrine cell circuits perform specific visual computations.

Recent developments in induced pluripotent stem cell (iPSC) technology have enabled easier access to human cells in vitro. With increasing availability of human iPSC-derived neurons, both healthy and disease cell lines, screening compounds for neurodegenerative diseases on human cells can potentially be performed in the earlier stages of drug discovery. To accelerate the functional characterization of iPSC-derived neurons and the effect of compounds, reproducible and relevant results are necessary. In this webinar, the speakers will: Introduce high-resolution functional imaging of human iPSC-derived neurons Showcase how to extract functional features of hundreds of cells in a cell culture sample label-free Discuss electrophysiological parameters for characterizing the differences among several human neuronal cell lines

During brain development, neurons are born in specialized niches and migrate to target regions where they assemble to form the circuits that underlie mammalian behaviour. During their journey, neurons follow cell-intrinsic, genetic programs transmitted by their mother cells but also environmental cues, which together drive their maturation. Here, focusing on the neocortex, I will discuss recent findings from our laboratory in which we untangle and manipulate the programs at play in progenitors and their daughter neurons to better understand the emergence of cellular diversity in the developing brain.

Theoretical and computational models of neural systems have been traditionally focused on small neural circuits, given the lack of reliable data on large-scale brain structures. The situation has started to change in recent years, with novel recording technologies and large organized efforts to describe the brain at a larger scale. In this talk, Professor Mejias from the University of Amsterdam will review his recent work on developing anatomically constrained computational models of large-scale cortical networks of monkeys, and how this approach can help to answer important questions in large-scale neuroscience. He will focus on three main aspects: (i) the emergence of functional interactions in different frequency regimes, (ii) the role of balance for efficient large-scale communication, and (iii) new paradigms of brain function, such as working memory, in large-scale networks.

Bursts in gamma and other frequency ranges are thought to contribute to the efficiency of working memory or communication tasks. Abnormalities in bursts have also been associated with motor and psychiatric disorders. The determinants of burst generation are not known, specifically how single cell and connectivity parameters influence burst statistics and the corresponding brain states. We first present a generic mathematical model for burst generation in an excitatory-inhibitory (EI) network with self-couplings. The resulting equations for the stochastic phase and envelope of the rhythm’s fluctuations are shown to depend on only two meta-parameters that combine all the network parameters. They allow us to identify different regimes of amplitude excursions, and to highlight the supportive role that network finite-size effects and noisy inputs to the EI network can have. We discuss how burst attributes, such as their durations and peak frequency content, depend on the network parameters. In practice, the problem above follows the a priori challenge of fitting such E-I spiking networks to single neuron or population data. Thus, the second part of the talk will discuss a novel method to fit mesoscale dynamics using single neuron data along with a low-dimensional, and hence statistically tractable, single neuron model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous ‘pools’ of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using an E-I network of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived. We show that both single-neuron and connectivity parameters can be adequately recovered from simulated data.

During periods of persistent and inescapable stress, animals can switch from active to passive coping strategies to manage effort-expenditure. Such normally adaptive behavioural state transitions can become maladaptive in disorders such as depression. We developed a new class of multi-region recurrent neural network (RNN) models to infer brain-wide interactions driving such maladaptive behaviour. The models were trained to match experimental data across two levels simultaneously: brain-wide neural dynamics from 10-40,000 neurons and the realtime behaviour of the fish. Analysis of the trained RNN models revealed a specific change in inter-area connectivity between the habenula (Hb) and raphe nucleus during the transition into passivity. We then characterized the multi-region neural dynamics underlying this transition. Using the interaction weights derived from the RNN models, we calculated the input currents from different brain regions to each Hb neuron. We then computed neural manifolds spanning these input currents across all Hb neurons to define subspaces within the Hb activity that captured communication with each other brain region independently. At the onset of stress, there was an immediate response within the Hb/raphe subspace alone. However, RNN models identified no early or fast-timescale change in the strengths of interactions between these regions. As the animal lapsed into passivity, the responses within the Hb/raphe subspace decreased, accompanied by a concomitant change in the interactions between the raphe and Hb inferred from the RNN weights. This innovative combination of network modeling and neural dynamics analysis points to dual mechanisms with distinct timescales driving the behavioural state transition: early response to stress is mediated by reshaping the neural dynamics within a preserved network architecture, while long-term state changes correspond to altered connectivity between neural ensembles in distinct brain regions.

Scientists have worked to recreate human vision in computers for the past 50 years. But how much about human vision do we actually know? And can the brain be useful in furthering computer vision? This talk will take a look at the similarities and differences between (modern) computer vision and human vision, as well as the important crossovers, collaborations, and applications that define the interface between computational neuroscience and computer vision. If you want to know more about how the brain sees (really sees), how computer vision developments are inspired by the brain, or how to apply AI to neuroscience, this talk is for you.

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

Creative thought relies on the reorganisation of existing knowledge. Sleep is known to be important for creative thinking, but there is a debate about which sleep stage is most relevant, and why. I will address this issue by proposing that Rapid Eye Movement sleep, or 'REM', and Non-REM sleep facilitate creativity in different ways. Memory replay mechanisms in Non-REM can abstract rules from corpuses of learned information, while replay in REM may promote novel associations. I propose that the iterative interleaving of REM and Non-REM across a night boosts the formation of complex knowledge frameworks, and allows these frameworks to be restructured - thus facilitating creative thought. My talk will discuss experiments exploring these hypotheses, and the mechanisms for these processes.

Dravet syndrome is a severe childhood epilepsy due to heterozygous loss-of-function mutation of the gene SCN1A, which encodes the type 1 neuronal voltage gated sodium (Na+) channel alpha-subunit Nav1.1. Prior studies in mouse models of Dravet syndrome (Scn1a+/- mice) at early developmental time points indicate that, in cerebral cortex, Nav1.1 is predominantly expressed in GABAergic interneurons (INs) and, in particular, in parvalbumin-positive fast-spiking basket cells (PV-INs). This has led to a model of Dravet syndrome pathogenesis whereby Nav1.1 mutation leads to preferential IN dysfunction, decreased synaptic inhibition, hyperexcitability, and epilepsy. We found that, at later developmental time points, the intrinsic excitability of PV-INs has essentially normalized, via compensatory reorganization of axonal Na+ channels. Instead, we found persistent and seemingly paradoxical dysfunction of putative disinhibitory INs expressing vasoactive intestinal peptide (VIP-INs). In vivo two-photon calcium imaging in neocortex during temperature-induced seizures in Scn1a+/- mice showed that mean activity of both putative principal cells and PV-INs was higher in Scn1a+/- relative to wild-type controls during quiet wakefulness at baseline and at elevated core body temperature. However, wild-type PV-INs showed a progressive synchronization in response to temperature elevation that was absent in PV-INs from Scn1a+/- mice immediately prior to seizure onset. We suggest that impaired PV-IN synchronization, perhaps via persistent axonal dysfunction, may contribute to the transition to the ictal state during temperature induced seizures in Dravet syndrome.