Neuroscience seminars

Malformations of the human cerebral cortex represent a major cause of developmental disabilities. To date, animal models carrying mutations of genes so far identified in human patients with brain malformations only partially recapitulate the expected phenotypes and therefore do not provide reliable models to entirely understand the molecular and cellular mechanisms responsible for these disorders. Hence, we combine the in vivo mouse model and the human brain organoids in order to better comprehend the mechanisms involved in the migration of neurons during human development and tackle the causes of neurodevelopmental disorders. Our results show that we can model human brain development and disorders using human brain organoids and contribute to open new avenues to bridge the gap of knowledge between human brain malformations and existing animal models.

The thalamic reticular nucleus (TRN), the major source of thalamic inhibition, is known to regulate thalamocortical interactions critical for sensory processing, attention and cognition. TRN dysfunction has been linked to sensory abnormality, attention deficit and sleep disturbance across multiple neurodevelopmental disorders. Currently, little is known about the organizational principles underlying its divergent functions. In this talk, I will start with an example of how dysfunction of TRN contributes to attention deficit and sleep disruption using a mouse model of Ptchd1 mutation, which in humans cause neurodevelopmental disorder with ASD. Building on these findings, we further performed an integrative single-cell analysis linking molecular and electrophysiological features of the TRN to connectivity and systems-level function. We identified two subnetworks of the TRN with segregated anatomical structure, distinct electrophysiological properties, differential connections to the functionally distinct first-order and higher-order thalamic nuclei, and differential role in regulating sleep. These studies provide a comprehensive atlas for TRN neurons at the single-cell resolution and a foundation for studying diverse functions and dysfunctions of the TRN. Finally, I will describe the newly developed minimally invasive optogenetic tool for probing circuit function and dysfunction.

The Open Science movement advocates for more transparent, equitable and reliable science. It focusses on improving existing infrastructures and spans all aspects of the scientific process, from implementing systems that reward pre-registering studies and guarantee their publication, all the way to making research data citable and freely available. In this context, open source tools (and the development ethos supporting them) are becoming more and more present in academic labs, as researchers are realizing that they can improve the quality of their work, while cutting costs. In this talk an overview of OS tools for neuroscience will be given, with a focus on software and hardware, and how their use can bring scientific independence and make research evolve faster.

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.

Understanding how neural circuits in the brain compute information not only requires determining how individual inhibitory and excitatory elements of circuits are wired together, but also a detailed knowledge of their functional interactions. Recent advances in optogenetic techniques and mouse genetics now offer ways to specifically probe the functional properties of neural circuits with unprecedented specificity. Perhaps one of the most heavily interrogated circuits in the mouse brain is one in the retina that is involved in coding direction (reviewed by Mauss et al., 2017; Vaney et al., 2012). In this circuit, direction is encoded by specialized direction-selective (DS) ganglion cells (DSGCs), which respond robustly to objects moving in a ‘preferred’ direction but not in the opposite or ‘null’ direction (Barlow and Levick, 1965). We now know this computation relies on the coordination of three transmitter systems: glutamate, GABA and acetylcholine (ACh). In this talk, I will discuss the synaptic mechanisms that produce the spatiotemporal patterns of inhibition and excitation that are crucial for shaping directional selectivity. Special emphasis will be placed on the role of ACh, as it is unclear whether it is mediated by synaptic or non-synaptic mechanisms, which is in fact a central issue in the CNS. Barlow, H.B., and Levick, W.R. (1965). The mechanism of directionally selective units in rabbit's retina. J Physiol 178, 477-504. Mauss, A.S., Vlasits, A., Borst, A., and Feller, M. (2017). Visual Circuits for Direction Selectivity. Annu Rev Neurosci 40, 211-230. Vaney, D.I., Sivyer, B., and Taylor, W.R. (2012). Direction selectivity in the retina: symmetry and asymmetry in structure and function. Nat Rev Neurosci 13, 194-208

The “Nature versus Nurture” debate on the origin of behaviour has long been dominated by a genome versus experience dichotomy. However, evidence that genetically identical individuals kept under identical conditions are behaviourally different is incontrovertible. Where might such individuality come from? Neither genes nor the environment directly encode behaviour. They encode or influence processes, notably the development of neuronal circuits, that in turn control behaviour. An understanding of how neuronal circuits develop and function at the individual organism level is therefore essential for understanding the origin of individuals. I will discuss our efforts to address this issue over the past decade using the Drosophila fruit fly as a model system.

Working memory is the sketchpad of consciousness, the fundamental mechanism the brain uses to gain volitional control over its thoughts and actions. For the past 50 years, working memory has been thought to rely on cortical neurons that fire continuous impulses that keep thoughts “online”. However, new work from our lab has revealed more complex dynamics. The impulses fire sparsely and interact with brain rhythms of different frequencies. Higher frequency gamma (> 35 Hz) rhythms help carry the contents of working memory while lower frequency alpha/beta (~8-30 Hz) rhythms act as control signals that gate access to and clear out working memory. In other words, a rhythmic dance between brain rhythms may underlie your ability to control your own thoughts.

Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system of the mouse for technological applications. Pedro and co-researchers have successfully extracted odor identity and concentration from olfactory receptor neuron low-dimensional activity trajectories. They have further developed a novel method to identify a shared latent space that allowed decoding of odor information across animals.

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.

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.