Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as `rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by -- and in turn adaptively affect -- prefrontal dynamics.

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as 'rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and in turn adaptively affect - prefrontal dynamics.

Humans and animals learn and plan with flexibility and efficiency well beyond that of modern Machine Learning methods. This is hypothesized to owe in part to the ability of animals to build structured representations of their environments, and modulate these representations to rapidly adapt to new settings. In the first part of this talk, I will discuss theoretical work describing how learned representations in hippocampus enable rapid adaptation to new goals by learning predictive representations, while entorhinal cortex compresses these predictive representations with spectral methods that support smooth generalization among related states. I will also cover recent work extending this account, in which we show how the predictive model can be adapted to the probabilistic setting to describe a broader array of generalization results in humans and animals, and how entorhinal representations can be modulated to support sample generation optimized for different behavioral states. In the second part of the talk, I will overview some of the ways in which we have combined many of the same mathematical concepts with state-of-the-art deep learning methods to improve efficiency and performance in machine learning applications like physical simulation, relational reasoning, and design.

Sensory neurons of the vagus nerve monitor distention and stretch in the gastrointestinal tract. We used genetically guided anatomical tracing, optogenetics and electrophysiology to identify and characterize two vagal sensory neuronal subtypes expressing Prox2 and Runx3. We show that these neuronal subtypes innervate the esophagus where they display regionalized innervation patterns. Electrophysiological analyses showed that they are both low threshold mechanoreceptors but possess different adaptation properties. Lastly, genetic ablation of Prox2 and Runx3 neurons demonstrated their essential roles for esophageal peristalsis and swallowing in freely behaving animals. Our work reveals the identity and function of the vagal neurons that provide mechanosensory feedback from the esophagus to the brain and could lead to better understanding and treatment of esophageal motility disorders.

Humans and other animals can estimate rapidly the number of items in a scene, flashes or tones in a sequence and motor actions. Adaptation techniques provide clear evidence in humans for the existence of specialized numerosity mechanisms that make up the numbersense. This sense of number is truly general, encoding the numerosity of both spatial arrays and sequential sets, in vision and audition, and interacting strongly with action. The adaptation (cross-sensory and cross-format) acts on sensory mechanisms rather than decisional processes, pointing to a truly general sense.

Many engineered and biological systems must trade off performance and energy use, and the brain is no exception. While there are theories on how activity levels are controlled in biological networks through feedback control (homeostasis), it is not clear what the effects on population coding are, and therefore how performance and energy can be traded off. In this talk we will consider this tradeoff in auto-encoding networks, in which there is a clear definition of performance (the coding loss). We first show how SNNs follow a characteristic trade-off curve between activity levels and coding loss, but that standard networks need to be retrained to achieve different tradeoff points. We next formalize this tradeoff with a joint loss function incorporating coding loss (performance) and activity loss (energy use). From this loss we derive a class of spiking networks which coordinates its spiking to minimize both the activity and coding losses -- and as a result can dynamically adjust its coding precision and energy use. The network utilizes several known activity control mechanisms for this --- threshold adaptation and feedback inhibition --- and elucidates their potential function within neural circuits. Using geometric intuition, we demonstrate how these mechanisms regulate coding precision, and thereby performance. Lastly, we consider how these insights could be transferred to trained SNNs. Overall, this work addresses a key energy-coding trade-off which is often overlooked in network studies, expands on our understanding of homeostasis in biological SNNs, as well as provides a clear framework for considering performance and energy use in artificial SNNs.

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes, but it is not known whether these subtypes have correspondingly diverse activity patterns in the living brain. We show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 Subclasses, 11 Types, and 35 Subtypes using previously-defined transcriptomic clusters. Responses to visual stimuli differed significantly only across Subclasses, suppressing cells in the Sncg Subclass while driving cells in the other Subclasses. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory Subtypes that fired more in resting, oscillatory brain states have less axon in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro and express more inhibitory cholinergic receptors. Subtypes firing more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 Subtypes shape state-dependent cortical processing.

Animals traversing different environments encounter both stable background stimuli and novel cues, which are generally thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Sensory adaptation is a neural mechanism that filters background by minimizing responses to stable sensory stimuli, and a fundamental feature of sensory systems. Adaptation over relatively fast timescales (milliseconds to minutes) have been reported in many sensory systems. However, adaptation to persistent environmental stimuli over longer timescales (hours to days) have been largely unexplored, even though those timescales are ethologically important since animals typically stay in one environment for hours. I showed that each of the ~1,000 olfactory sensory neuron (OSN) subtypes in the mouse harbors a distinct transcriptome whose content is precisely determined by interactions between its odorant receptor and the environment. This transcriptional variation is systematically organized to support sensory adaptation: expression levels of many genes relevant to transforming odors into spikes continuously vary across OSN subtypes, dynamically adjust to new environments over hours, and accurately predict acute OSN-specific odor responses. The sensory periphery therefore separates salient signals from predictable background via a transcriptional mechanism whose moment-to-moment state reflects the past and constrains the future; these findings suggest a general model in which structured transcriptional variation within a cell type reflects individual experience.

Tinbergen encouraged ethologists to address animal behaviour by answering four questions, covering physiology, adaptation, phylogeny, and development. This broad approach has implications for neuroscience and psychology, yet, questions about phylogeny are rarely considered in these fields. Here I describe how phylogeny can shed light on our understanding of brain structure and function. Further, I show that we now have or are developing the data and analytical methods necessary to study the natural history of the human mind.

The research in my lab focuses on sensory signal processing, particularly in cases where sensory systems perform at or near the limits imposed by physics. Photon counting in the visual system is a beautiful example. At its peak sensitivity, the performance of the visual system is limited largely by the division of light into discrete photons. This observation has several implications for phototransduction and signal processing in the retina: rod photoreceptors must transduce single photon absorptions with high fidelity, single photon signals in photoreceptors, which are only 0.03 – 0.1 mV, must be reliably transmitted to second-order cells in the retina, and absorption of a single photon by a single rod must produce a noticeable change in the pattern of action potentials sent from the eye to the brain. My approach is to combine quantitative physiological experiments and theory to understand photon counting in terms of basic biophysical mechanisms. Fortunately there is more to visual perception than counting photons. The visual system is very adept at operating over a wide range of light intensities (about 12 orders of magnitude). Over most of this range, vision is mediated by cone photoreceptors. Thus adaptation is paramount to cone vision. Again one would like to understand quantitatively how the biophysical mechanisms involved in phototransduction, synaptic transmission, and neural coding contribute to adaptation.

In behaving rodents, CA1 pyramidal neurons receive spatially-tuned depolarizing synaptic input while traversing a specific location within an environment called its place. Midbrain dopamine neurons participate in reinforcement learning, and bursts of action potentials riding a depolarizing wave of synaptic input signal rewards and reward expectation. Interestingly, slice electrophysiology in vitro shows that both types of cells exhibit a pronounced reduction in firing rate (adaptation) and even cessation of firing during sustained depolarization. We included a five state Markov model of NaV1.6 (for CA1) and NaV1.2 (for dopamine neurons) respectively, in computational models of these two types of neurons. Our simulations suggest that long-term inactivation of this channel is responsible for the adaptation in CA1 pyramidal neurons, in response to triangular depolarizing current ramps. We also show that the differential contribution of slow inactivation in two subpopulations of midbrain dopamine neurons can account for their different dynamic ranges, as assessed by their responses to similar depolarizing ramps. These results suggest long-term inactivation of the sodium channel is a general mechanism for adaptation.

Circadian clocks dominate our lives. By creating and distributing an internal representation of 24-hour solar time, they prepare us, and thereby adapt us, to the daily and seasonal world. Jet-lag is an obvious indicator of what can go wrong when such adaptation is disrupted acutely. More seriously, the growing prevalence of rotational shift-work which runs counter to our circadian life, is a significant chronic challenge to health, presenting as increased incidence of systemic conditions such as metabolic and cardiovascular disease. Added to this, circadian and sleep disturbances are a recognised feature of various neurological and psychiatric conditions, and in some cases may contribute to disease progression. The “head ganglion” of the circadian system is the suprachiasmatic nucleus (SCN) of the hypothalamus. It synchronises the, literally, innumerable cellular clocks across the body, to each other and to solar time. Isolated in organotypic slice culture, it can maintain precise, high-amplitude circadian cycles of neural activity, effectively, indefinitely, just as it does in vivo. How is this achieved: how does this clock in a dish work? This presentation will consider SCN time-keeping at the level of molecular feedback loops, neuropeptidergic networks and neuron-astrocyte interactions.

The pupil provides a rich, non-invasive measure of the neural bases of perception and cognition, and has been of particular value in uncovering the role of arousal-linked neuromodulation, which alters cortical processing as well as pupil size. But pupil size is subject to a multitude of influences, which complicates unique interpretation. We measured pupils of observers experiencing perceptual multistability -- an ever-changing subjective percept in the face of unchanging but inconclusive sensory input. In separate conditions the endogenously generated perceptual changes were either task-relevant or not, allowing a separation between perception-related and task-related pupil signals. Perceptual changes were marked by a complex pupil response that could be decomposed into two components: a dilation tied to task execution and plausibly indicative of an arousal-linked noradrenaline surge, and an overlapping constriction tied to the perceptual transient and plausibly a marker of altered visual cortical representation. Constriction, but not dilation, amplitude systematically depended on the time interval between perceptual changes, possibly providing an overt index of neural adaptation. These results show that the pupil provides a simultaneous reading on interacting but dissociable neural processes during perceptual multistability, and suggest that arousal-linked neuromodulation shapes action but not perception in these circumstances. This presentation covers work that was published in e-life

In order for animals to thrive in their complex environments, their sensory systems must form representations of objects that are invariant to changes in some dimensions of their physical cues. For example, we can recognize a friend’s speech in a forest, a small office, and a cathedral, even though the sound reaching our ears will be very different in these three environments. I will discuss our recent experiments into how neurons in auditory cortex can form stable representations of sounds in this acoustically varied world. We began by using a normative computational model of hearing to examine how the brain may recognize a sound source across rooms with different levels of reverberation. The model predicted that reverberations can be removed from the original sound by delaying the inhibitory component of spectrotemporal receptive fields in the presence of stronger reverberation. Our electrophysiological recordings then confirmed that neurons in ferret auditory cortex apply this algorithm to adapt to different room sizes. Our results demonstrate that this neural process is dynamic and adaptive. These studies provide new insights into how we can recognize auditory objects even in highly reverberant environments, and direct further research questions about how reverb adaptation is implemented in the cortical circuit.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

Recently, the field of computational neuroscience has seen an explosion of the use of trained recurrent network models (RNNs) to model patterns of neural activity. These RNN models are typically characterized by tuned recurrent interactions between rate 'units' whose dynamics are governed by smooth, continuous differential equations. However, the response of biological single neurons is better described by all-or-none events - spikes - that are triggered in response to the processing of their synaptic input by the complex dynamics of their membrane. One line of research has attempted to resolve this discrepancy by linking the average firing probability of a population of simplified spiking neuron models to rate dynamics similar to those used for RNN units. However, challenges remain to account for complex temporal dependencies in the biological single neuron response and for the heterogeneity of synaptic input across the population. Here, we make progress by showing how to derive dynamic rate equations for a population of spiking neurons with multi-timescale adaptation properties - as this was shown to accurately model the response of biological neurons - while they receive independent time-varying inputs, leading to plausible asynchronous activity in the network. The resulting rate equations yield an insightful segregation of the population's response into dynamics that are driven by the mean signal received by the neural population, and dynamics driven by the variance of the input across neurons, with respective timescales that are in agreement with slice experiments. Further, these equations explain how input variability can shape log-normal instantaneous rate distributions across neurons, as observed in vivo. Our results help interpret properties of the neural population response and open the way to investigating whether the more biologically plausible and dynamically complex rate model we derive could provide useful inductive biases if used in an RNN to solve specific tasks.

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory systems should dynamically allocate limited resources when the goal is to optimally infer changing latent states of the environment, rather than reconstruct incoming stimuli. We use these ideas to explore dynamic tradeoffs between the efficiency and speed of sensory adaptation schemes, and the downstream computations that these schemes might support. Finally, we derive families of codes that balance these competing objectives, and we demonstrate their close match to experimentally-observed neural dynamics during sensory adaptation. These results provide a unifying perspective on adaptive neural dynamics across a range of sensory systems, environments, and sensory tasks.

Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.

Last year’s SNUFA workshop report concluded “Moving toward neuron numbers comparable with biology and applying these networks to real-world data-sets will require the development of novel algorithms, software libraries, and dedicated hardware accelerators that perform well with the specifics of spiking neural networks” [1]. Taking inspiration from machine learning libraries — where techniques such as parallel batch training minimise latency and maximise GPU occupancy — as well as our previous research on efficiently simulating SNNs on GPUs for computational neuroscience [2,3], we are extending our GeNN SNN simulator to pursue this vision. To explore GeNN’s potential, we use the eProp learning rule [4] — which approximates RTRL — to train SNN classifiers on the Spiking Heidelberg Digits and the Spiking Sequential MNIST datasets. We find that the performance of these classifiers is comparable to those trained using BPTT [5] and verify that the theoretical advantages of neuron models with adaptation dynamics [5] translate to improved classification performance. We then measured execution times and found that training an SNN classifier using GeNN and eProp becomes faster than SpyTorch and BPTT after less than 685 timesteps and much larger models can be trained on the same GPU when using GeNN. Furthermore, we demonstrate that our implementation of parallel batch training improves training performance by over 4⨉ and enables near-perfect scaling across multiple GPUs. Finally, we show that performing inference using a recurrent SNN using GeNN uses less energy and has lower latency than a comparable LSTM simulated with TensorFlow [6].

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.

Spike-driven adaptation involves intracellular mechanisms that are initiated by spiking and lead to the subsequent reduction of spiking rate. One of its consequences is the temporal patterning of spike trains, as it imparts serial correlations between interspike intervals in baseline activity. Surprisingly the hidden adaptation states that lead to these correlations themselves exhibit quasi-independence. This talk will first discuss recent findings about the role of such adaptation in suppressing noise and extending sensory detection to weak stimuli that leave the firing rate unchanged. Further, a matching of the post-synaptic responses to the pre-synaptic adaptation time scale enables a recovery of the quasi-independence property, and can explain observations of correlations between post-synaptic EPSPs and behavioural detection thresholds. We then consider the involvement of spike-driven adaptation in the representation of intervals between sensory events. We discuss the possible link of this time-stamping mechanism to the conversion of egocentric to allocentric coordinates. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus.

Spatial neglect is a syndrome that is most frequently associated with damage to the right hemisphere, although damage to the left hemisphere can also result in signs of spatial neglect. It is characterised by absent or deficient awareness of the contralesional side of space. The screening and diagnosis of spatial neglect lacks a universal gold standard, but is usually achieved by using various modes of assessment. Spatial neglect is also difficult to treat, although prism adaptation training (PAT) has in the past reportedly showed some promise. This seminar will include highlights from a series of studies designed to identify knowledge gaps, and will suggest ways in which these can be bridged. The first study was conducted to identify and quantify clinicians’ use of assessment tools for spatial neglect, finding that several different tools are in use, but that there is an emerging consensus and appetite for harmonisation. The second study included PAT, and sought to uncover whether PAT can improve engagement in recommended therapy in order to improve the outcomes of stroke survivors with spatial neglect. The final study, a systematic review and meta-analysis, sought to investigate the scientific efficacy (rather than clinical effectiveness) of PAT, identifying several knowledge gaps in the existing literature and a need for a new approach in the study of PAT in the clinical setting.

At any given moment the brain receives more sensory information than it can use to guide adaptive behaviour, creating the need for mechanisms that promote efficient processing of incoming sensory signals. One way in which the brain might reduce its sensory processing load is to encode successive presentations of the same stimulus in a more efficient form, a process known as neural adaptation. Conversely, when a stimulus violates an expected pattern, it should evoke an enhanced neural response. Such a scheme for sensory encoding has been formalised in predictive coding theories, which propose that recent experience establishes expectations in the brain that generate prediction errors when violated. In this webinar, Professor Jason Mattingley will discuss whether the encoding of elementary visual features is modulated when otherwise identical stimuli are expected or unexpected based upon the history of stimulus presentation. In humans, EEG was employed to measure neural activity evoked by gratings of different orientations, and multivariate forward modelling was used to determine how orientation selectivity is affected for expected versus unexpected stimuli. In mice, two-photon calcium imaging was used to quantify orientation tuning of individual neurons in the primary visual cortex to expected and unexpected gratings. Results revealed enhanced orientation tuning to unexpected visual stimuli, both at the level of whole-brain responses and for individual visual cortex neurons. Professor Mattingley will discuss the implications of these findings for predictive coding theories of sensory encoding. Professor Jason Mattingley is a Laureate Fellow and Foundation Chair in Cognitive Neuroscience at The University of Queensland. His research is directed toward understanding the brain processes that support perception, selective attention and decision-making, in health and disease.

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).

My laboratory studies how the retina processes visual scenes and transmits this information to the brain. We use multi-electrode arrays to record the activity of hundreds of retina neurons simultaneously in conjunction with transgenic mouse lines and chemogenetics to manipulate neural circuit function. We are interested in three major areas. First, we work to understand how neurons in the retina are functionally connected. Second we are studying how light-adaptation and circadian rhythms alter visual processing in the retina. Finally, we are working to understand the mechanisms of retinal degenerative conditions and we are investigating potential treatments in animal models.

Behavior relies on the ability of sensory systems to infer changing properties of the environment from incoming sensory stimuli. However, the demands that detecting and adjusting to changes in the environment place on a sensory system often differ from the demands associated with performing a specific behavioral task. This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory systems should dynamically allocate limited resources when the goal is to optimally infer changing latent states of the environment, rather than reconstruct incoming stimuli. We use these ideas to explore dynamic tradeoffs between the efficiency and speed of sensory adaptation schemes, and the downstream computations that these schemes might support. Finally, we derive families of codes that balance these competing objectives, and we demonstrate their close match to experimentally-observed neural dynamics during sensory adaptation. These results provide a unifying perspective on adaptive neural dynamics across a range of sensory systems, environments, and sensory tasks.

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.

Black Americans experience diverse racism-related stressors throughout the lifespan. Disproportionately high trauma exposure, economic disadvantage, explicit racism and inequitable treatment are stressors faced by many Black Americans. These experiences have a cumulative negative impact on psychological and physical health. However, little is understood about how experiences of racism, such as discrimination, can mediate health outcomes via their effects on neurobiology. I will present clinical, behavioral, physiological and neurobiological data from Black American participants in the Grady Trauma Project, a longstanding study of trauma conducted in inner-city Atlanta. These data will be discussed in the context of both risk and resilience/adaptation perspectives. Finally, recommendations for future clinical neuroscience research and targets for intervention in marginalized populations will be discussed.

Designing artificially intelligent systems and interfaces with socio-emotional skills is a challenging task. Progress in industry and developments in academia provide us a positive outlook, however, the artificial social and emotional intelligence of the current technology is still limited. My lab’s research has been pushing the state of the art in a wide spectrum of research topics in this area, including the design and creation of new datasets; novel feature representations and learning algorithms for sensing and understanding human nonverbal behaviours in solo, dyadic and group settings; designing longitudinal human-robot interaction studies for wellbeing; and investigating how to mitigate the bias that creeps into these systems. In this talk, I will present some of my research team’s explorations in these areas including social appropriateness of robot actions, virtual reality based cognitive training with affective adaptation, and bias and fairness in data-driven emotionally intelligent systems.

The vIRt nucleus in the medulla, composed of mainly inhibitory neurons, is necessary for whisking rhythm generation. It innervates motoneurons in the facial nucleus (FN) that project to intrinsic vibrissa muscles. The nearby pre-Bötzinger complex (pBötC), which generates inhalation, sends inhibitory inputs to the vIRt nucleus which contribute to the synchronization of vIRt neurons. Lower-amplitude periodic whisking, however, can occur after decay of the pBötC signal. To explain how vIRt network generates these “intervening” whisks by bursting in synchrony, and how pBötC input induces strong whisks, we construct and analyze a conductance-based (CB) model of the vIRt circuit composed of hypothetical two groups, vIRtr and vIRtp, of bursting inhibitory neurons with spike-frequency adaptation currents and constant external inputs. The CB model is reduced to a rate model to enable analytical treatment. We find, analytically and computationally, that without pBötC input, periodic bursting states occur within a certain ranges of network connectivities. Whisk amplitudes increase with the level constant external input to the vIRT. With pBötC inhibition intact, the amplitude of the first whisk in a breathing cycle is larger than the intervening whisks for large pBötC input and small inhibitory coupling between the vIRT sub-populations. The pBötC input advances the next whisk and shortens its amplitude if it arrives at the beginning of the whisking cycle generated by the vIRT, and delays the next whisks if it arrives at the end of that cycle. Our theory provides a mechanism for whisking generation and reveals how whisking frequency and amplitude are controlled.

Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating four general problems. How are circuits designed to integrate information efficiently? How do sensory adaptation and synaptic plasticity contribute to efficiency? How do the sizes of neurons and networks relate to energy consumption and representational capacity? To what extent have energy costs shaped neurons, sense organs and brain regions during evolution?

Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.

The long-term goal of my research is to study the mammalian retina as a model for the central nervous system (CNS) -- to understand how it functions in physiological conditions, how it is formed, how it breaks down in pathological conditions, and how it can be repaired. I have focused on two research themes: 1) Photoreceptor structure, synapse, circuits, and development, 2) Hibernation and metabolic adaptations in the retina and beyond. As the first neuron of the visual system, photoreceptors are vital for photoreception and transmission of visual signals. I am particularly interested in cone photoreceptors, as they mediate our daylight vision with high resolution color information. Diseases affecting cone photoreceptors compromise visual functions in the central macular area of the human retina and are thus most detrimental to our vision. However, because cones are much less abundant compared to rods in most mammals, they are less well studied. We have used the ground squirrel (GS) as a model system to study cone vision, taking advantage of their unique cone-dominant retina. In particular, we have focused on short-wavelength sensitive cones (S-cones), which are not only essential for color vision, but are also an important origin of signals for biological rhythm, mood and cognitive functions, and the growth of the eye during development. We are studying critical cone synaptic structures – synaptic ribbons, the synaptic connections of S-cones, and the development of S-cones with regard to their specific connections. These works will provide knowledge of normal retinal development and function, which can also be extended to the rest of CNS; for example, the mechanisms of synaptic targeting during development. In addition, such knowledge will benefit the development of optimal therapeutic strategies for regeneration and repair in cases of retinal degenerative disease. Many neurodegenerative diseases, including retinal diseases, are rooted in metabolic stress in neurons and/or glial cells. Using the same GS model, we aim to learn from this hibernating mammal, which possesses an amazing capability to adapt to the extreme metabolic conditions during hibernation. By exploring the mechanisms of such adaptation, we hope to discover novel therapeutic tactics for neurodegenerative diseases.

Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at climate change and how it poses a significant threat to life on our planet. In the second, we will consider the potential of multi-agent systems for climate change mitigation and adaptation. And finally, in the third, we will discuss recent research from InstaDeep into better understanding the behaviour of networked MARL systems used for CPR management. More specifically, we will see how the tools from empirical game-theoretic analysis may be harnessed to analyse the differences in networked MARL systems. The results give new insights into the consequences associated with certain design choices and provide an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.

Vision starts in the retina where images are transformed and coded into neuronal activity relevant for the brain. These coding steps function optimally over a wide range of conditions: from bright day on the beach to a moonless night. Under these very different conditions, specific retinal mechanisms continue to select relevant aspects of the visual world and send this information to the brain. We are studying the neuronal processing involved in these selection and adaptation processes. This knowledge is essential for understanding how the visual system works and forms the basis for research dedicated to restoring vision in blind people.

How does a circuit of neurons process sensory information? And how are transformations of neural signals altered by changes in synaptic strength? We investigate these questions in the context of the visual system and the lateral line of fish. A distinguishing feature of our approach is the imaging of activity across populations of synapses – the fundamental elements of signal transfer within all brain circuits. A guiding hypothesis is that the plasticity of neurotransmission plays a major part in controlling the input-output relation of sensory circuits, regulating the tuning and sensitivity of neurons to allow adaptation or sensitization to particular features of the input. Sensory systems continuously adjust their input-output relation according to the recent history of the stimulus. A common alteration is a decrease in the gain of the response to a constant feature of the input, termed adaptation. For instance, in the retina, many of the ganglion cells (RGCs) providing the output produce their strongest responses just after the temporal contrast of the stimulus increases, but the response declines if this input is maintained. The advantage of adaptation is that it prevents saturation of the response to strong stimuli and allows for continued signaling of future increases in stimulus strength. But adaptation comes at a cost: a reduced sensitivity to a future decrease in stimulus strength. The retina compensates for this loss of information through an intriguing strategy: while some RGCs adapt following a strong stimulus, a second population gradually becomes sensitized. We found that the underlying circuit mechanisms involve two opposing forms of synaptic plasticity in bipolar cells: synaptic depression causes adaptation and facilitation causes sensitization. Facilitation is in turn caused by depression in inhibitory synapses providing negative feedback. These opposing forms of plasticity can cause simultaneous increases and decreases in contrast-sensitivity of different RGCs, which suggests a general framework for understanding the function of sensory circuits: plasticity of both excitatory and inhibitory synapses control dynamic changes in tuning and gain.