Seminars

This panel discussion aims to generate meaningful dialogue between emerging leaders in basic and clinical neuroscience. It promises to talk about the ground realities and what acts as a hindrance in people to people connection in the field. It aims to advocate for policy change that will revolutionize the field of neuroscience, allowing neuroscientists to collaborate with clinicians wherein the new research can be made available for public use

Dr. Carmen Sandi leads the laboratory of Behavioral Genetis in EPFL, Lausanne. Her lab investigates the impact and mechanism whereby stress and anxiety affect brain and behavior in an integrative program involvong studies in rodents and humans. She is the founder and co-president of Swiss Stress Network, co-director of Swiss National Centre of Competence in Research Synapsy. She is Chair of the ALBA Network, and pas-President of Cajal Advanced Neuroscience Training Program and the Federation of European Neuroscience Societies.

The cerebellum permits a wide range of behaviors that involve sensorimotor integration. We have been investigating how specific cellular and synaptic specializations of cerebellar neurons measured in vitro, give rise to circuit activity in vivo. We have investigated these issues by studying Purkinje neurons as well as the large neurons of the mouse cerebellar nuclei, which form the major excitatory premotor projection from the cerebellum. Large CbN cells have ion channels that favor spontaneous action potential firing and GABAA receptors that generate ultra-fast inhibitory synaptic currents, raising the possibility that these biophysical attributes may permit CbN cells to respond differently to the degree of temporal coherence of their Purkinje cell inputs. In vivo, self-initiated motor programs associated with whisking correlates with asynchronous changes in Purkinje cell simple spiking that are asynchronous across the population. The resulting inhibition converges with mossy fiber excitation to yield little change in CbN cell firing, such that cerebellar output is low or cancelled. In contrast, externally applied sensory stimuli elicits a transient, synchronous inhibition of Purkinje cell simple spiking. During the resulting strong disinhibition of CbN cells, sensory-induced excitation from mossy fibers effectively drives cerebellar outputs that increase the magnitude of reflexive whisking. Purkinje cell synchrony, therefore, may be a key variable contributing to the “positive effort” hypothesized by David Marr in 1969 to be necessary for cerebellar control of movement.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

TheVirtualBrain (TVB; thevirtualbrain.org) is a software platform for simulating whole-brain network dynamics. TVB models link biophysical parameters at the cellular level with systems-level functional neuroimaging signals. Data available from animal models can provide vital constraints for the linkage across spatial and temporal scales. I will describe the construction of a macaque cortical connectome as an initial step towards a comprehensive multi-scale macaque TVB model. I will also describe our process of validating the connectome and show an example simulation of macaque resting-state dynamics using TVB. This connectome opens the opportunity for the addition of other available data from the macaque, such as electrophysiological recordings and receptor distributions, to inform multi-scale models of brain dynamics. Future work will include extensions to neurological conditions and other nonhuman primate species.

Learning in real life is often sequential: A learner first learns task A, then task B. If the tasks are related, the learner may adapt the previously learned representation instead of generating a new one from scratch. Adaptation may ease learning task B but may also decrease the performance on task A. Such interference has been observed in experimental and machine learning studies. In the latter case, it is mediated by correlations between weight updates for the different tasks. In typical applications, like image classification with feed-forward networks, these correlated weight updates can be traced back to input correlations. For many neuroscience tasks, however, networks need to not only transform the input, but also generate substantial internal dynamics. Here we illuminate the role of internal dynamics for interference in recurrent neural networks (RNNs). We analyze RNNs trained sequentially on neuroscience tasks with gradient descent and observe forgetting even for orthogonal tasks. We find that the degree of interference changes systematically with tasks properties, especially with emphasis on input-driven over autonomously generated dynamics. To better understand our numerical observations, we thoroughly analyze a simple model of working memory: For task A, a network is presented with an input pattern and trained to generate a fixed point aligned with this pattern. For task B, the network has to memorize a second, orthogonal pattern. Adapting an existing representation corresponds to the rotation of the fixed point in phase space, as opposed to the emergence of a new one. We show that the two modes of learning – rotation vs. new formation – are directly linked to recurrent vs. input-driven dynamics. We make this notion precise in a further simplified, analytically tractable model, where learning is restricted to a 2x2 matrix. In our analysis of trained RNNs, we also make the surprising observation that, across different tasks, larger random initial connectivity reduces interference. Analyzing the fixed point task reveals the underlying mechanism: The random connectivity strongly accelerates the learning mode of new formation, and has less effect on rotation. The prior thus wins the race to zero loss, and interference is reduced. Altogether, our work offers a new perspective on sequential learning in recurrent networks, and the emphasis on internally generated dynamics allows us to take the history of individual learners into account.

When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.

Lifelong devotion to headache research has led to many discoveries. First a series of studies of brain blood flow during attacks of migraine. The results showed changes compatible with cortical spreading depression in migraine without aura effectively negating the then prevailing vasospastic/ischemic theory. In migraine without aura no changes in brain blood flow. This difference was crucial for the separation of migraine with aura and migraine without aura in the first and subsequent editions of the international headache classification headed by me. Then a human migraine provocation model that has elucidated the molecular mechanisms of migraine. Successively we showed in series of papers the importance of nitric oxide, histamine, CGRP, PACAP and prostanoids. Therapeutic effectiveness of antagonizing these provokers by tonabersat, L-NMMA, CGRP receptor antagonists and monoclonal antibodies and of NSAIDs. Present and future attempts to put all these signaling mechanisms into a framework but it is not easy

The human visual system enables us to recognize objects (e.g., this is a cup) and act upon them (e.g., grasp the cup) with astonishing ease and accuracy. For decades, it was widely accepted that these different functions rely on two separated cortical pathways. The ventral occipitotemporal pathway subserves object recognition, while the dorsal occipitoparietal pathway promotes visually guided actions. In my talk, I will discuss recent evidence from a series of neuropsychological, developmental and neuroimaging studies that were aimed to explore the nature of object representations in the dorsal pathway. The results from these studies highlight the plausible role of the dorsal pathway in object perception and reveal an interplay between shape representations derived by the two pathways. Together, these findings challenge the binary distinction between the two pathways and are consistent with the view that object recognition is not the sole product of ventral pathway computations, but instead relies on a distributed network of regions.

Rapidly changing inputs such as visual scenes and auditory landscapes are transmitted over several synaptic interfaces and perceived with little loss of detail, but individual neurons are typically “noisy” and cortico-cortical connections are typically “weak”. To understand how information embodied in spike train is transmitted in a lossless manner, we focus on a single synaptic interface: between pyramidal cells and putative interneurons. Using arbitrary white noise patterns injected intra-cortically as photocurrents to freely-moving mice, we find that directly-activated cells exhibit precision of several milliseconds, but post-synaptic, indirectly-activated cells exhibit higher precision. Considering multiple identical messages, the reliability of directly-activated cells peaks at a timescale of dozens of milliseconds, whereas indirectly-activated cells exhibit an order-of-magnitude faster timescale. Using data-driven modelling, we find that error correction is consistent with non-linear amplification of coincident spikes.

Investigations of the neural systems mediating the processing of social groups defined by race, specifically Black and White race groups in American participants, reveals significant overlap with brain mechanisms involved in emotion. This talk will provide an overview of research on the neuroscience of race and emotion, focusing on implicit race attitudes. Implicit race attitudes are expressed without conscious effort and control, and contrast with explicit, conscious attitudes. In spite of sharp decline in the expression of explicit, negative attitudes towards outgroup race members over the last half century, negative implicit attitudes persist, even in the face of strong egalitarian goals and beliefs. Early research demonstrated that implicit, but not explicit, negative attitudes towards outgroup race members correlate with blood oxygenation level dependent (BOLD) signal in the amygdala – a region implicated in threat representations, as well as emotion’s influence on cognition. Building on this initial finding, we demonstrate how learning and decisions may be modulated by implicit race attitudes and involve neural systems mediating emotion, learning and choice. Finally, we discuss techniques that may diminish the unintentional expression of negative, implicit race attitudes.

The journey of development begins with sperm swimming through the female reproductive tract en-route to the egg. In order to successfully complete this journey sperm must beat a single flagellum, propelling themselves through a wide range of fluids, from liquified semen to viscous cervical mucus. It is well-known that the beating tail is driven by an array of 9 microtubule doublets surrounding a central pair, with interconnecting dynein motors generating shear forces and driving elastic wave propagation. Despite this knowledge, the exact mechanism by which coordination of these motors drives oscillating waves along the flagellum remains unknown; hypothesised mechanisms include curvature control, sliding control, and geometric clutch. In this talk we will discuss the mechanisms of flagellar bending, and present a simple model of active curvature that is able to produce many of the various sperm waveforms that are seen experimentally, including those in low and high viscosity fluids and after a cell has ‘hyperactivated’ (a chemical process thought to be key for fertilization). We will show comparisons between these simulated waveforms and sperm that have been experimentally tracked, and discuss methods for fitting simulated mechanistic parameters to these real cells.

Can alterations in experience trigger different plastic modifications in neuronal structure and function, and if so, how do they integrate at the cellular level? To address this question, we interrogated circuitry in the mouse olfactory bulb responsible for the earliest steps in odor processing. We induced experience-dependent plasticity in mice of either sex by blocking one nostril for one day, a minimally invasive manipulation that leaves the sensory organ undamaged and is akin to the natural transient blockage suffered during common mild rhinal infections. We found that such brief sensory deprivation produced structural and functional plasticity in one highly specialized bulbar cell type: axon-bearing dopaminergic neurons in the glomerular layer. After 24 h naris occlusion, the axon initial segment (AIS) in bulbar dopaminergic neurons became significantly shorter, a structural modification that was also associated with a decrease in intrinsic excitability. These effects were specific to the AIS-positive dopaminergic subpopulation because no experience-dependent alterations in intrinsic excitability were observed in AIS-negative dopaminergic cells. Moreover, 24 h naris occlusion produced no structural changes at the AIS of bulbar excitatory neurons, mitral/tufted and external tufted cells, nor did it alter their intrinsic excitability. By targeting excitability in one specialized dopaminergic subpopulation, experience-dependent plasticity in early olfactory networks might act to fine-tune sensory processing in the face of continually fluctuating inputs. (https://www.jneurosci.org/content/41/10/2135)

Psychotic disorders are devastating conditions without any mechanistic treatment available. One major hurdle in the biological study of psychosis is the challenge of rigorously probing this condition in pre-clinical animal models. The goal of our research is to develop and exploit innovative frameworks for the study of psychosis in mice. In our present work, where we developed a cross-species computational psychiatry approach to probe hallucination-like perception. This enabled us to directly relate human and mouse behavior, and to demonstrate and dissect the causal role of striatal dopamine in hallucination-like perception. Our results suggest a neural circuit mechanism for the long-standing dopamine hypothesis of psychosis, and provide a new translational framework for the biological study of psychosis. This opens up exciting possibilities for advancing the biological understanding of psychosis and to identify mechanistic treatment targets.