Neuroscience seminars

Learning is a population phenomenon. Since it is the organized activity of populations of neurons that cause movement, learning a new skill must involve reshaping those population activity patterns. Seeing how the brain does this has been elusive, but a brain-computer interface approach can yield new insight. We presented monkeys with novel BCI mappings that we knew would be difficult for them to learn how to control. Over several days, we observed the emergence of new patterns of neural activity that endowed the animals with the ability to perform better at the BCI task. We speculate that there also exists a direct relationship between new patterns of neural activity and new abilities during natural movements, but it is much harder to see in that setting.

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. We investigated how the brain coordinates its activity across areas to inform complex, top-down control behaviors. Animals were trained to perform a novel brain machine interface task to guide a visual cursor to a reward zone, using activity recorded with widefield calcium imaging. This allowed us to screen for cortical areas implicated in causal neural control of the visual object. Animals could decorrelate normally highly-correlated areas to perform the task, and used an explore-exploit search in neural activity space to discover successful strategies. Higher visual and parietal areas were more active during the task in expert animals. Single unit recordings targeted to these areas indicated that the sensory representation of an object was sensitive to an animal’s subjective sense of controlling it.

By studying the neural correlates of hand augmentation, we are exploring the boundaries of neuroplasticity seeing how it can be harnessed to improve the usability and control of prosthetic devices. Tamar Makin and Dani Clode each discuss their research and perspectives within the field of prosthetics that has led to this unique collaboration and exploration of motor augmentation and the brain.

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

Emerging genetic studies of late-onset Alzheimer’s Disease implicate the brain’s resident macrophages in the pathogenesis of AD. More than half the risk genes associated with late-onset AD are selectively expressed in microglia and peripheral myeloid cells; yet we know little about the underlying biology or how myeloid cells contribute to AD pathogenesis. Using single-cell RNA sequencing and spatial transcriptomics we identified molecular signatures that can be used to localize and monitor distinct microglia functional states in the human and mouse brain. Our results show that microglia assume diverse functional states in development, aging and injury, including populations corresponding to known microglial functions including proliferation, migration, inflammation, and synaptic phagocytosis. We identified several innate immune pathways by which microglia recognize and prune synapses during development and in models of Alzheimer’s disease, including the classical complement cascade. Illuminating the mechanisms by which developing synaptic circuits are sculpted is providing important insight on understanding how to protect synapses in Alzheimer’s and other neurodegenerative diseases of synaptic dysfunction.

Glioblastoma cells trigger pharmacoresistant seizures that may promote tumor growth and diminish the quality of remaining life. To define the relationship between growth of glial tumors and their neuronal microenvironment, and to identify genomic biomarkers and mechanisms that may point to better prognosis and treatment of drug resistant epilepsy in brain cancer, we are analyzing a new generation of genetically defined CRISPR/in utero electroporation inborn glioblastoma (GBM) tumor models engineered in mice. The molecular pathophysiology of glioblastoma cells and surrounding neurons and untransformed astrocytes are compared at serial stages of tumor development. Initial studies reveal that epileptiform EEG spiking is a very early and reliable preclinical signature of GBM expansion in these mice, followed by rapidly progressive seizures and death within weeks. FACS-sorted transcriptomic analysis of cortical astrocytes reveals the expansion of a subgroup enriched in pro-synaptogenic genes that may drive hyperexcitability, a novel mechanism of epileptogenesis. Using a prototypical GBM IUE model, we systematically define and correlate the earliest appearance of cortical hyperexcitability with progressive cortical tumor cell invasion, including spontaneous episodes of spreading cortical depolarization, innate inflammation, and xCT upregulation in the peritumoral microenvironment. Blocking this glutamate exporter reduces seizure load. We show that the host genome contributes to seizure risk by generating tumors in a monogenic deletion strain (MapT/tau -/-) that raises cortical seizure threshold. We also show that the tumor variant profile determines epilepsy risk. Our genetic dissection approach sets the stage to broadly explore the developmental biology of personalized tumor/host interactions in mice engineered with novel human tumor mutations in specified glial cell lineages.

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand precisely how neural circuit computations mechanistically cause behavior. Establishing this type of causal understanding will require multiple perturbational (e.g optogenetic) experiments. It has been unclear exactly how many such experiments are needed and how this number scales with the size of the nervous system in question. Here, using techniques from Theoretical Computer Science, we prove that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in many cases, unless a widely-posited hypothesis about computation is false (i.e. unless P = NP). Furthermore, using data and estimates, we demonstrate that the feasible experimental regime is typically one where the number of experiments performable scales sub-linearly in the number of neurons in the nervous system. This remarkable gulf between the worst-case and the feasible suggests an algorithmic barrier to such an understanding. Determining which notions of understanding are algorithmically tractable to establish in what contexts, thus, becomes an important new direction for investigation. TL; DR: Non-existence of tractable algorithms for neural circuit interrogation could pose a barrier to comprehensively understanding how neural circuits cause behavior. Preprint: https://biorxiv.org/content/10.1101/639724v1/…

Abstract semantic relations (e.g., category membership, part-whole, antonymy, cause-effect) are central to human intelligence, underlying the distinctively human ability to reason by analogy. I will describe a computational project (Bayesian Analogy with Relational Transformations) that aims to extract explicit representations of abstract semantic relations from non-relational inputs automatically generated by machine learning. BART’s representations predict patterns of typicality and similarity for semantic relations, as well as similarity of neural signals triggered by semantic relations during analogical reasoning. In this approach, analogy emerges from the ability to learn and compare relations; mapping emerges later from the ability to compare patterns of relations.

Transposable elements, known colloquially as ‘jumping genes’, constitute approximately 45% of the human genome. Cells utilize epigenetic defenses to limit transposable element jumping, including formation of silencing heterochromatin and generation of piwi-interacting RNAs (piRNAs), small RNAs that facilitate clearance of transposable element transcripts. We have utilized fruit flies, mice and postmortem human brain samples to identify transposable element dysregulation as a key mediator of neuronal death in tauopathies, a group of neurodegenerative disorders that are pathologically characterized by deposits of tau protein in the brain. Mechanistically, we find that heterochromatin decondensation and reduction of piwi and piRNAs drive transposable element dysregulation in tauopathy. We further report a significant increase in transcripts of the endogenous retrovirus class of transposable elements in human Alzheimer’s disease and progressive supranuclear palsy, suggesting that transposable element dysregulation is conserved in human tauopathy. Taken together, our data identify heterochromatin decondensation, piwi and piRNA depletion and consequent transposable element dysregulation as a pharmacologically targetable, mechanistic driver of neurodegeneration in tauopathy.

To function properly, the nervous system consumes vast amounts of energy, which is mostly provided by carbohydrate metabolism. Neurons are very sensitive to changes in the extracellular fluid surrounding them, which necessitated shielding of the nervous system from fluctuating solute concentrations in circulation. This is achieved by the blood-brain barrier (BBB) that prevents paracellular diffusion of solutes into the nervous system. This in turn also means that all nutrients that are needed e.g. for sufficient energy supply need to be transported over the BBB. We use Drosophila as a model system to better understand the metabolic homeostasis in the central nervous system. Glial cells play essential roles in both nutrient uptake and neural energy metabolism. Carbohydrate transport over the glial BBB is well-regulated and can be adapted to changes in carbohydrate availability. Furthermore, Drosophila glial cell are highly glycolytic cells that support the rather oxidative metabolism of neurons. Upon perturbations of carbohydrate metabolism, the glial cells prove to be metabolically very flexible and able to adapt to changing circumstances. I will summarize what we know about carbohydrate transport at the Drosophila BBB and about the metabolic coupling between neurons and glial cells. Our data shows that many basic features of neural metabolism are well conserved between the fly and mammals.

Animals are able to perceive self-motion and navigate in their environment using optic flow information. They often perform visually guided stabilization behaviors like the optokinetic (OKR) or optomotor response (OMR) in order to maintain their eye and body position relative to the moving surround. But how does the animal manage to perform appropriate behavioral response and how are processing tasks divided between the various non-cortical visual brain areas? Experiments have shown that the zebrafish pretectum, which is homologous to the mammalian accessory optic system, is involved in the OKR and OMR. The optic tectum (superior colliculus in mammals) is involved in processing of small stimuli, e.g. during prey capture. We have previously shown that many pretectal neurons respond selectively to rotational or translational motion. These neurons are likely detectors for specific optic flow patterns and mediate behavioral choices of the animal based on optic flow information. We investigate the motion feature extraction of brain structures that receive input from retinal ganglion cells to identify the visual computations that underlie behavioral decisions during prey capture, OKR, OMR and other visually mediate behaviors. Our study of receptive fields shows that receptive field sizes in pretectum (large) and tectum (small) are very different and that pretectal responses are diverse and anatomically organized. Since calcium indicators are slow and receptive fields for motion stimuli are difficult to measure, we also develop novel stimuli and statistical methods to infer the neuronal computations of visual brain areas.

Both computational and experimental results in single neurons and small networks demonstrate that very similar network function can result from quite disparate sets of neuronal and network parameters. Using the crustacean stomatogastric nervous system, we study the influence of these differences in underlying structure on differential resilience of individuals to a variety of environmental perturbations, including changes in temperature, pH, potassium concentration and neuromodulation. We show that neurons with many different kinds of ion channels can smoothly move through different mechanisms in generating their activity patterns, thus extending their dynamic range. The talk will be simultaneously translated to spanish by the interpreter Liliana Viera, MSc. Los resultados tanto computacionales como experimentales en neuronas individuales y redes pequeñas demuestran que funcionamientos de redes muy similares pueden pueden resultar de conjuntos bastante dispares de parámetros neuronales y de las redes. Utilizando el sistema nervioso estomatogástrico de los crustáceos, estudiamos la influencia de estas diferencias en la estructura subyacente en la resistencia diferencial de los individuos a una variedad de perturbaciones ambientales, incluidos los cambios de temperatura, pH, concentración de potasio y neuromodulación. Mostramos que neuronas con muchos tipos diferentes de canales iónicos pueden moverse suavemente a través de diferentes mecanismos para generar sus patrones de actividad, extendiendo así su rango dinámico. La conferencia será traducida simultáneamente al español por la intérprete Liliana Viera MSc.

Inhibitory interneurons come in different classes and form intricate circuits. While our knowledge of these circuits has advanced substantially over the last decades, it is not fully understood how the structure of these circuits relates to their function. I will present some of our recent attempts to “understand” the structure of interneuron circuits by means of computational modeling. Surprisingly (at least for us), we found that prominent features of inhibitory circuitry can be accounted for by an optimisation for excitation-inhibition (E/I) balance. In particular, we find that such an optimisation generates networks that resemble mouse V1 in terms of the structure of synaptic efficacies between principal cells and parvalbumin-positive interneurons. Moreover, an optimisation for E/I balance across neuronal compartments promotes a functional diversification of interneurons into two classes that resemble parvalbumin and somatostatin-positive interneurons. Time permitting, I may briefly touch on recent work in which we link E/I balance to prediction error coding in V1.

Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of information flow between them. Noise is ubiquitous in neural systems, and often considered an irritant to be overcome. In the first part of this talk, I will suggest that noise could play a functional role in synaptic pruning, allowing the brain to probe network structure and determine which synapses are redundant. I will introduce a simple, local, unsupervised plasticity rule that either strengthens or prunes synapses using only synaptic weight and the noise-driven covariance of the neighboring neurons. For a subset of linear and rectified-linear networks, this rule provably preserves the spectrum of the original matrix and hence preserves network dynamics even when the fraction of pruned synapses asymptotically approaches 1. The plasticity rule is biologically-plausible and may suggest a new role for noise in neural computation. Time permitting, I will then turn to the problem of extracting structure from neural population data sets using dimensionality reduction methods. I will argue that nonlinear structures naturally arise in neural data and show how these nonlinearities cause linear methods of dimensionality reduction, such as Principal Components Analysis, to fail dramatically in identifying low-dimensional structure.

In mammals, the selective transformation of transient experience into stored memory occurs in the hippocampus, which develops representations of specific events in the context in which they occur. In this talk, I will focus on the development of hippocampal circuits and the self-organized dynamics embedded in them since the latter critically support the role of the hippocampus in memory. I will discuss evidence that adult hippocampal cells and circuits are remarkably sculpted by development, as early as embryonic neurogenesis. We argue that these primary developmental programs provide a scaffold onto which later experience of the external world can be grafted. Next, I will present data on the emergence of recurrent connectivity and self-organized dynamics in hippocampal circuits and outline the critical turn points and discontinuities in that developmental journey.

Chemotherapeutic drugs (used for treating cancer), HIV infection and antiretroviral therapy (ART) can independently cause difficult-to-manage painful neuropathy. Paclitaxel, a chemotherapeutic drug, for example is associated with high incidence of peripheral neuropathy, around 71% of the patients of which 27% of these develop neuropathic pain. Use of cannabis or phytocannabinoids has been reported to improve pain measures in patients with neuropathic pain, including painful HIV-associated sensory neuropathy and cancer pain. Phytocannabinoids and endocannabinoids, such as anandamide and 2-arachidonoylglycerol (2-AG), produce their effects via cannabinoid (CB) receptors, which are present both in the periphery and central nervous system. Endocannabinoids are synthesized in an “on demand” fashion and are degraded by various enzymes such as fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL). Various studies, including those from our group, suggest that there are changes in gene and protein expression of endocannabinoid molecules during chemotherapy-induced neuropathic pain (CINP), HIV and antiretroviral-induced neuropathic pain. Analysis of endocannabinoid molecule expression in the brain, spinal cord and paw skin using LC-MS/MS show that there is a specific deficiency of the endocannabinoids 2-AG and/or anandamide in the periphery during CINP. Various drugs including endocannabinoids, cannabidiol, inhibitors of FAAH and MGL, CB receptor agonists, desipramine and coadministered indomethacin plus minocycline have been found to either prevent the development and/or attenuate established CINP, HIV and antiretroviral-induced neuropathic pain in a CB receptor-dependent manner. The results available suggest that targeting the endocannabinoid system for prevention and treatment of CINP, HIV-associated neuropathic pain and antiretroviral-induced neuropathic pain is a plausible therapeutic option.