Update on vestibular, ocular motor and cerebellar disorders

Preserving microbial diversity as a keystone of human and planetary health

Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally show that our memory-equipped SNN model outperforms state-of-the-art deep learning mechanisms in a sequential pattern-memorization task, as well as demonstrate superior out-of-distribution generalization capabilities compared to these models. We further show that our model can be successfully applied to one-shot learning and classification of handwritten characters, improving over the state-of-the-art SNN model. We also demonstrate the capability of our model to learn associations for audio to image synthesis from spoken and handwritten digits. Our SNN model further presents a novel solution to a variety of cognitive question answering tasks from a standard benchmark, achieving comparable performance to both memory-augmented ANN and SNN-based state-of-the-art solutions to this problem. Finally we demonstrate that our model is able to learn from rewards on an episodic reinforcement learning task and attain near-optimal strategy on a memory-based card game. Hence, our results show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. Since local Hebbian plasticity can easily be implemented in neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.

Never too late: music-induced brain and cognitive benefits in healthy elderly

Chemistry of the adaptive mind: lessons from dopamine

The human brain faces a variety of computational dilemmas, including the flexibility/stability, the speed/accuracy and the labor/leisure tradeoff. I will argue that striatal dopamine is particularly well suited to dynamically regulate these computational tradeoffs depending on constantly changing task demands. This working hypothesis is grounded in evidence from recent studies on learning, motivation and cognitive control in human volunteers, using chemical PET, psychopharmacology, and/or fMRI. These studies also begin to elucidate the mechanisms underlying the huge variability in catecholaminergic drug effects across different individuals and across different task contexts. For example, I will demonstrate how effects of the most commonly used psychostimulant methylphenidate on learning, Pavlovian and effortful instrumental control depend on fluctuations in current environmental volatility, on individual differences in working memory capacity and on opportunity cost respectively.

The Synaptome Architecture of the Brain: Lifespan, disease, evolution and behavior

The overall aim of my research is to understand how the organisation of the synapse, with particular reference to the postsynaptic proteome (PSP) of excitatory synapses in the brain, informs the fundamental mechanisms of learning, memory and behaviour and how these mechanisms go awry in neurological dysfunction. The PSP indeed bears a remarkable burden of disease, with components being disrupted in disorders (synaptopathies) including schizophrenia, depression, autism and intellectual disability. Our work has been fundamental in revealing and then characterising the unprecedented complexity (>1000 highly conserved proteins) of the PSP in terms of the subsynaptic architecture of postsynaptic proteins such as PSD95 and how these proteins assemble into complexes and supercomplexes in different neurons and regions of the brain. Characterising the PSPs in multiple species, including human and mouse, has revealed differences in key sets of functionally important proteins, correlates with brain imaging and connectome data, and a differential distribution of disease-relevant proteins and pathways. Such studies have also provided important insight into synapse evolution, establishing that vertebrate behavioural complexity is a product of the evolutionary expansion in synapse proteomes that occurred ~500 million years ago. My lab has identified many mutations causing cognitive impairments in mice before they were found to cause human disorders. Our proteomic studies revealed that >130 brain diseases are caused by mutations affecting postsynaptic proteins. We uncovered mechanisms that explain the polygenic basis and age of onset of schizophrenia, with postsynaptic proteins, including PSD95 supercomplexes, carrying much of the polygenic burden. We discovered the “Genetic Lifespan Calendar”, a genomic programme controlling when genes are regulated. We showed that this could explain how schizophrenia susceptibility genes are timed to exert their effects in young adults. The Genes to Cognition programme is the largest genetic study so far undertaken into the synaptic molecular mechanisms underlying behaviour and physiology. We made important conceptual advances that inform how the repertoire of both innate and learned behaviours is built from unique combinations of postsynaptic proteins that either amplify or attenuate the behavioural response. This constitutes a key advance in understanding how the brain decodes information inherent in patterns of nerve impulses, and provides insight into why the PSP has evolved to be so complex, and consequently why the phenotypes of synaptopathies are so diverse. Our most recent work has opened a new phase, and scale, in understanding synapses with the first synaptome maps of the brain. We have developed next-generation methods (SYNMAP) that enable single-synapse resolution molecular mapping across the whole mouse brain and extensive regions of the human brain, revealing the molecular and morphological features of a billion synapses. This has already uncovered unprecedented spatiotemporal synapse diversity organised into an architecture that correlates with the structural and functional connectomes, and shown how mutations that cause cognitive disorders reorganise these synaptome maps; for example, by detecting vulnerable synapse subtypes and synapse loss in Alzheimer’s disease. This innovative synaptome mapping technology has huge potential to help characterise how the brain changes during normal development, including in specific cell types, and with degeneration, facilitating novel pathways to diagnosis and therapy.

Do Capuchin Monkeys, Chimpanzees and Children form Overhypotheses from Minimal Input? A Hierarchical Bayesian Modelling Approach

Abstract concepts are a powerful tool to store information efficiently and to make wide-ranging predictions in new situations based on sparse data. Whereas looking-time studies point towards an early emergence of this ability in human infancy, other paradigms like the relational match to sample task often show a failure to detect abstract concepts like same and different until the late preschool years. Similarly, non-human animals have difficulties solving those tasks and often succeed only after long training regimes. Given the huge influence of small task modifications, there is an ongoing debate about the conclusiveness of these findings for the development and phylogenetic distribution of abstract reasoning abilities. Here, we applied the concept of “overhypotheses” which is well known in the infant and cognitive modeling literature to study the capabilities of 3 to 5-year-old children, chimpanzees, and capuchin monkeys in a unified and more ecologically valid task design. In a series of studies, participants themselves sampled reward items from multiple containers or witnessed the sampling process. Only when they detected the abstract pattern governing the reward distributions within and across containers, they could optimally guide their behavior and maximize the reward outcome in a novel test situation. We compared each species’ performance to the predictions of a probabilistic hierarchical Bayesian model capable of forming overhypotheses at a first and second level of abstraction and adapted to their species-specific reward preferences.

Analogical Reasoning with Neuro-Symbolic AI

Knowledge discovery with computers requires a huge amount of search. Analogical reasoning is effective for efficient knowledge discovery. Therefore, we proposed analogical reasoning systems based on first-order predicate logic using Neuro-Symbolic AI. Neuro-Symbolic AI is a combination of Symbolic AI and artificial neural networks and has features that are easy for human interpretation and robust against data ambiguity and errors. We have implemented analogical reasoning systems by Neuro-symbolic AI models with word embedding which can represent similarity between words. Using the proposed systems, we efficiently extracted unknown rules from knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning.

Life and death of transient neurons in the development of functional and dysfunctional cortical circuits

Understanding the role of neural heterogeneity in learning

The brain has a hugely diverse and heterogeneous nature. The exact role of heterogeneity has been relatively little explored as most neural models tend to be largely homogeneous. We trained spiking neural networks with varying degrees of heterogeneity on complex real-world tasks and found that heterogeneity resulted in more stable and robust training and improved training performance, especially for tasks with a higher temporal structure. Moreover, the optimal distribution of parameters found by training was found to be similar to experimental observations. These findings suggest that heterogeneity is not simply a result of noisy biological processes, but it may play a crucial role for learning in complex, changing environments.

How inclusive and diverse is non-invasive brain stimulation in the treatment of psychiatric disorders?

How inclusive and diverse is non-invasive brain stimulation in the treatment of psychiatric disorders?Indira Tendolkar, Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry. Mental illness is associated with a huge socioeconomic burden worldwide, with annual costs only in the Netherlands of €22 billion. Over two decades of cognitive and affective neuroscience research with modern tools of neuroimaging and neurophysiology in humans have given us a wealth of information about neural circuits underlying the main symptom domains of psychiatric disorders and their remediation. Neuromodulation entails the alteration of these neural circuits through invasive (e.g., DBS) or non-invasive (e.g., TMS) techniques with the aim of improving symptoms and/or functions and enhancing neuroplasticity. In my talk, I will focus on neuromodulation studies using repetitive transcranial magnetic stimulation (rTMS) as a relatively safe, noninvasive method, which can be performed simultaneously with neurocognitive interventions. Using the examples of two chronifying mental illnesses, namely obsessive compulsive disorders and major depressive disorder (MDD), I will review the concept of "state dependent" effects of rTMS and highlight how simultaneous or sequential cognitive interventions could help optimize rTMS therapy by providing further control of ongoing neural activity in targeted neural networks. Hardly any attention has been paid to diversity aspects in the studies. By including studies from low- and middle income countries, I will discuss the potential of non-invasive brain stimulation from a transcultural perspective.

Neural mechanisms of navigation behavior

The regions of the insect brain devoted to spatial navigation are beautifully orderly, with a remarkably precise pattern of synaptic connections. Thus, we can learn much about the neural mechanisms of spatial navigation by targeting identifiable neurons in these networks for in vivo patch clamp recording and calcium imaging. Our lab has recently discovered that the "compass system" in the Drosophila brain is anchored to not only visual landmarks, but also the prevailing wind direction. Moreover, we found that the compass system can re-learn the relationship between these external sensory cues and internal self-motion cues, via rapid associative synaptic plasticity. Postsynaptic to compass neurons, we found neurons that conjunctively encode heading direction and body-centric translational velocity. We then showed how this representation of travel velocity is transformed from body- to world-centric coordinates at the subsequent layer of the network, two synapses downstream from compass neurons. By integrating this world-centric vector-velocity representation over time, it should be possible for the brain to form a stored representation of the body's path through the environment.

Hughlings Jackson Lecture: Making Progress in Progressive MS – the Ultimate Challenge!

On April 22, 2021, Dr. Alan J Thompson of the University College London and the UCL Institute of Neurology, London, UK will deliver the Hughlings Jackson Lecture entitled, “Making Progress in Progressive MS – the Ultimate Challenge!” Established in 1935, the Hughlings Jackson Lecture is The Neuro’s premier scientific lecture. It honors the legacy of British neurologist John Hughlings Jackson (1835-1911) who pioneered the development of neurology as a medical specialty. Talk Abstract : The international focus on progressive MS, driven by the Progressive MS Alliance amongst others, together with recent encouraging results from clinical trials have raised the profile and emphasised the importance of understanding, treating and ultimately preventing progression in MS. Effective treatment for Progressive MS is now regarded as the single most important issue facing the MS community. There are several important challenges to developing new treatments for progressive MS. Fundamental to any development in treatment is a better understanding of the mechanisms of tissue injury underpinning progression which will in turn allow the identification of new targets against which treatments can be directed. There are additional complications in determining when progression actually starts, determining the impact of aging and defining the progressive clinical phenotypes – an area which has become increasingly complex in recent months. Evaluating potential new treatments in progressive MS also poses particular challenges including trial design and the selection of appropriate clinical and imaging outcomes - in particular, identifying an imaging biomarker for phase II trials of progressive MS. Despite these challenges, considerable progress is being made in developing new treatments targeting the innate immune system and exploring neuroprotective strategies. Further advances are being driven by a number of international networks, funded by the Progressive MS Alliance. Overall we are seeing encouraging progress as a result of co-ordinated global collaboration which offers real possibilities for truly effective treatment of progression.

Visual Circuits For Action an evolutionary perspective

Neural heterogeneity promotes robust learning

The brain has a hugely diverse, heterogeneous structure. By contrast, many functional neural models are homogeneous. We compared the performance of spiking neural networks trained to carry out difficult tasks, with varying degrees of heterogeneity. Introducing heterogeneity in membrane and synapse time constants substantially improved task performance, and made learning more stable and robust across multiple training methods, particularly for tasks with a rich temporal structure. In addition, the distribution of time constants in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example

This webinar is mainly focused on “Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example”. Biomarkers and Biotypes of Drug Addiction: funding opportunities at NIDA, Tanya Ramey (NIDA, US) Neuroimaging-based Biomarker Development for Clinical Trials, Owen Carmicheal (Pennington Biomedical Research Center, USA) ENIGMA-Addiction Cue Reactivity Initiative (ACRI) and Checklist, Hamed Ekhtiari (Laureate Institute for Brain Research, USA) ENIGMA-ACRI Checklist: Participant Characteristics, General fMRI Information, General Task Information, Cue Information, Task-related Assessments, Pre-Post Scanning Consideration (James Prisciandaro, Medical University of South Carolina, USA; Marc Kaufman, McLean Hospital/Harvard Medical School, USA; Anna Zilverstand, University of Minnesota; Torsten Wüstenberg, Charité Medical University Berlin, Germany; Falk Kiefer, University of Heidelberg, Germany; Amy Janes, Harvard Medical School, USA) How to Add fMRI Drug Cue Reactivity to the ENIGMA Consortium: Road Ahead, Hugh Garavan, University of Vermont)

Parallel ascending spinal pathways for affective touch and pain

Each day we experience myriad somatosensory stimuli: hugs from loved ones, warm showers, a mosquito bite, and sore muscles after a workout. These tactile, thermal, itch, and nociceptive signals are detected by peripheral sensory neuron terminals distributed throughout our body, propagated into the spinal cord, and then transmitted to the brain through ascending spinal pathways. Primary sensory neurons that detect a wide range of somatosensory stimuli have been identified and characterized. In contrast, very little is known about how peripheral signals are integrated and processed within the spinal cord and conveyed to the brain to generate somatosensory perception and behavioral responses. We tackled this question by developing new mouse genetic tools to define projection neuron (PN) subsets of the anterolateral pathway, a major ascending spinal cord pathway, and combining these new tools with advanced anatomical, physiological, and behavioral approaches. We found that Gpr83+ PNs, a newly identified subset of spinal cord output neurons, and Tacr1+ PNs are largely non-overlapping populations that innervate distinct sets of subnuclei within the lateral parabrachial nucleus (PBNL) of the pons in a zonally segregated manner. In addition, Gpr83+ PNs are highly sensitive to cutaneous mechanical stimuli, receive strong synaptic inputs from primary mechanosensory neurons, and convey tactile information bilaterally to the PBNL in a non-topographically organized manner. Remarkably, Gpr83+ mechanosensory limb of the anterolateral pathway controls behaviors associated with different hedonic values (appetitive or aversive) in a scalable manner. This is the first study to identify a dedicated spinal cord output pathway that conveys affective touch signals to the brain and to define parallel ascending circuit modules that cooperate to convey tactile, thermal and noxious cutaneous signals from the spinal cord to the brain. This study has also revealed exciting new therapeutic opportunities for developing treatments for neurological disorders associated with pain and affective touch.

The role of spatiotemporal waves in coordinating regional dopamine decision signals

The neurotransmitter dopamine is essential for normal reward learning and motivational arousal processes. Indeed these core functions are implicated in the major neurological and psychiatric dopamine disorders such as schizophrenia, substance abuse disorders/addiction and Parkinson's disease. Over the years, we have made significant strides in understanding the dopamine system across multiple levels of description, and I will focus on our recent advances in the computational description, and brain circuit mechanisms that facilitate the dual role of dopamine in learning and performance. I will specifically describe our recent work with imaging the activity of dopamine axons and measurements of dopamine release in mice performing various behavioural tasks. We discovered wave-like spatiotemporal activity of dopamine in the striatal region, and I will argue that this pattern of activation supports a critical computational operation; spatiotemporal credit assignment to regional striatal subexperts. Our findings provide a mechanistic description for vectorizing reward prediction error signals relayed by dopamine.

Cortical population coding of consumption decisions

The moment that a tasty substance enters an animal’s mouth, the clock starts ticking. Taste information transduced on the tongue signals whether a potential food will nourish or poison, and the animal must therefore use this information quickly if it is to decide whether the food should be swallowed or expelled. The system tasked with computing this important decision is rife with cross-talk and feedback—circuitry that all but ensures dynamics and between-neuron coupling in neural responses to tastes. In fact, cortical taste responses, rather than simply reporting individual taste identities, do contain characterizable dynamics: taste-driven firing first reflects the substance’s presence on the tongue, and then broadly codes taste quality, and then shifts again to correlate with the taste’s current palatability—the basis of consumption decisions—all across the 1-1.5 seconds after taste administration. Ensemble analyses reveal the onset of palatability-related firing to be a sudden, nonlinear transition happening in many neurons simultaneously, such that it can be reliably detected in single trials. This transition faithfully predicts both the nature and timing of consumption behaviours, despite the huge trial-to-trial variability in both; furthermore, perturbations of this transition interfere with production of the behaviours. These results demonstrate the specific importance of ensemble dynamics in the generation of behaviour, and reveal the taste system to be akin to a range of other integrated sensorimotor systems.