Neuroscience seminars

Plasticity shapes the brain during development, and mechanisms of plasticity continue into adulthood to enable learning and memory. Nearly all brain functions are influenced by past events, reinforcing the view that the confluence of plasticity and computation in the same circuit elements is a core component of biological intelligence. My laboratory studies plasticity in the cerebral cortex during development, and plasticity during behaviour that is manifest as cortical dynamics. I will describe how cortical plasticity is implemented by learning rules that involve not only Hebbian changes and synaptic scaling but also dendritic renormalization. By using advanced techniques such as optical measurements of single-synapse function and structure in identified neurons in awake behaving mice, we have recently demonstrated locally coordinated plasticity in dendrites whereby specific synapses are strengthened and adjacent synapses with complementary features are weakened. Together, these changes cooperatively implement functional plasticity in neurons. Such plasticity relies on the dynamics of activity-dependent molecules within and between synapses. Alongside, it is increasingly clear that risk genes associated with neurodevelopmental disorders disproportionately target molecules of plasticity. Deficits in renormalization contribute fundamentally to dysfunctional neuronal circuits and computations, and may be a unifying mechanistic feature of these disorders.

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.

How nervous systems control behaviour is the main question we seek to answer in neuroscience. Although visual systems have been a popular entry point into the brain, we don’t understand—in any deep sense—how visual perception guides navigation in flies (or any organism). I will present recent progress towards this goal from our lab. We are using anatomical insights from connectomics, genetic methods for labelling and manipulating identified cell types, neurophysiology, behaviour, and computational modeling to explain how the fly brain processes visual motion to regulate behaviour.

The mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise robust way. If the thalamic units associated with each motif are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.

Many visual systems can process information in dynamically changing environments. In general, visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. This is achieved by adaptation. However, visual perception is challenged when adaptation is not fast enough to deal with sudden changes in overall illumination, for example when gaze follows a moving object from bright sunlight into a shaded area. We have recently shown that the visual system of the fly found a solution by propagating a corrective luminance-sensitive signal to higher processing stages. Using in vivo two-photon imaging and behavioural analyses we showed that distinct OFF-pathway inputs encode contrast and luminance. The luminance-sensitive pathway is particularly required when processing visual motion in contextual dim light, when pure contrast sensitivity underestimates the salience of a stimulus. Recent work in the lab has addressed the question how two visual pathways obtain such fundamentally different sensitivities, given common photoreceptor input. We are furthermore currently working out the network-based strategies by which luminance- and contrast-sensitive signals are combined to guide appropriate visual behaviour. Together, I will discuss the molecular, cellular, and circuit mechanisms that ensure contrast computation, and therefore robust vision, in fast changing visual scenes.

During conversations we rapidly switch between listening and speaking which often requires withholding or delaying our speech in order to hear others and avoid overlapping. This capacity for vocal turn-taking is exhibited by non-linguistic species as well, however the neural circuit mechanisms that enable us to regulate the precise timing of our vocalizations during interactions are unknown. We aim to identify the neural mechanisms underlying the coordination of vocal interactions. Therefore, we paired zebra finches with a vocal robot (1Hz call playback) and measured the bird’s call response times. We found that individual birds called with a stereotyped delay in respect to the robot call. Pharmacological inactivation of the premotor nucleus HVC revealed its necessity for the temporal coordination of calls. We further investigated the contributing neural activity within HVC by performing intracellular recordings from premotor neurons and inhibitory interneurons in calling zebra finches. We found that inhibition is preceding excitation before and during call onset. To test whether inhibition guides call timing we pharmacologically limited the impact of inhibition on premotor neurons. As a result zebra finches converged on a similar delay time i.e. birds called more rapidly after the vocal robot call suggesting that HVC inhibitory interneurons regulate the coordination of social contact calls. In addition, we aim to investigate the vocal turn-taking capabilities of the common nightingale. Male nightingales learn over 100 different song motifs which are being used in order to attract mates or defend territories. Previously, it has been shown that nightingales counter-sing with each other following a similar temporal structure to human vocal turn-taking. These animals are also able to spontaneously imitate a motif of another nightingale. The neural mechanisms underlying this behaviour are not yet understood. In my lab, we further probe the capabilities of these animals in order to access the dynamic range of their vocal turn taking flexibility.

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.

Olfactory navigation is essential for the survival of living beings from unicellular organisms to mammals. In the wild, rodents combine odor information with an internal spatial representation of the environment for foraging and navigation. What are the neural circuits in the brain that implement these behaviours? My research addresses this question by examining the synaptic circuits and neural population activity in the olfactory cortex to understand the integration of olfactory and spatial information. Primary olfactory (piriform) cortex (PCx) has long been recognized as a highly associative brain structure. What is the behavioural and functional role of these associative synapses in PCx? We designed an odor-cued navigation task, where rats must use both olfactory and spatial information to obtain water rewards. We recorded from populations of posterior piriform cortex (pPCx) neurons during behaviour and found that individual neurons were not only odor-selective, but also fired differentially to the same odor sampled at different locations, forming an “olfactory place map”. Spatial locations can be decoded from simultaneously recorded pPCx population, and spatial selectivity is maintained in the absence of odors, across behavioural contexts. This novel olfactory place map is consistent with our finding for a dominant role of associative excitatory synapses in shaping PCx representations, and suggest a role for PCx spatial representations in supporting olfactory navigation. This work not only provides insight into the neural basis for how odors can be used for navigation, but also reveals PCx as a prime site for addressing the general question of how sensory information is anchored within memory systems and combined with cognitive maps to guide flexible behaviour.

The ability to remember spatial environments is critical for everyday life. In this talk, I will discuss my lab’s findings on how the human brain supports spatial memory and navigation based on our experiments with direct brain recordings from neurosurgical patients performing virtual-reality spatial memory tasks. I will show that humans have a network of neurons that represent where we are located and trying to go. This network includes some cell types that are similar to those seen in animals, such as place and grid cells, as well as others that have not been seen before in animals, such as anchor and spatial-target cells. I also will explore the role of network oscillations in human memory, where humans again show several distinctive patterns compared to animals. Whereas rodents generally show a hippocampal oscillation at ~8Hz, humans have two separate hippocampal oscillations, at low and high frequencies, which support memory and navigation, respectively. Finally, I will show that neural oscillations in humans are traveling waves, propagating across the cortex, to coordinate the timing of neuronal activity across regions, which is another property not seen in animals. A theme from this work is that in terms of navigation and memory the human brain has novel characteristics compared with animals, which helps explain our rich behavioural abilities and has implications for treating disease and neurological disorders.

Animals rely on an internal sense of body position and movement to effectively control motor behaviour. This sense of proprioception is mediated by diverse populations of internal mechanosensory neurons distributed throughout the body. My lab is trying to understand how proprioceptive stimuli are detected by sensory neurons, integrated and transformed in central circuits, and used to guide motor output. We approach these questions using genetic tools, in vivo two-photon imaging, and patch-clamp electrophysiology in Drosophila. We recently found that the axons of fly leg proprioceptors are organized into distinct functional projections that contain topographic representations of specific kinematic features: one group of axons encodes tibia position, another encodes movement direction, and a third encodes bidirectional movement and vibration frequency. Whole-cell recordings from downstream neurons reveal that position, movement, and directional information remain segregated in central circuits. These feedback signals then converge upon motor neurons that control leg muscles during walking. Overall, our findings reveal how a low-dimensional stimulus – the angle of a single leg joint – is encoded by a diverse population of mechanosensory neurons. Specific proprioceptive parameters are initially processed by parallel pathways, but are ultimately integrated to influence motor output. This architecture may help to maximize information transmission, processing speed, and robustness, which are critical for feedback control of the limbs during adaptive locomotion.

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

First, we describe a new method for inferring the mental model of an animal performing a natural task. We use probabilistic methods to compute the most likely mental model based on an animal’s sensory observations and actions. This also reveals dynamic beliefs that would be optimal according to the animal’s internal model, and thus provides a practical notion of “rational thoughts.” Second, we construct a neural coding framework by which these rational thoughts, their computational dynamics, and actions can be identified within the manifold of neural activity. We illustrate the value of this approach by training an artificial neural network to perform a generalization of a widely used foraging task. We analyze the network’s behaviour to find rational thoughts, and successfully recover the neural properties that implemented those thoughts, providing a way of interpreting the complex neural dynamics of the artificial brain. Joint work with Zhengwei Wu, Minhae Kwon, Saurabh Daptardar, and Paul Schrater.

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.

The electrosensory lobe (ELL) in mormyrid electric fish is a cerebellar-like structure that cancels the sensory effects of self-generated electric fields, allowing prey to be detected. Like the cerebellum, the ELL involves two stages of processing, analogous to the Purkinje cells and cells of the deep cerebellar nuclei. Through the work of Curtis Bell and others, a model was previously developed to describe the output stage of the ELL, but the role of the Purkinje-cell analogs, the medium ganglion (MG) cells, in the circuit had remained mysterious. I will present a complete, multi-layer circuit description of the ELL, developed in collaboration with Nate Sawtell and Salomon Muller, that reveals a novel role for the MG cells. The resulting model provides an example of how a biological system solves well-known problems associated with learning in multi-layer networks, and it reveals that ELL circuitry is organization on the basis of learning rather than by the response properties of neurons.

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.