We are offering a full-time post-doctorate position for investigating pervasive intelligence and Artificial Intelligence of Things (AIoT). The research will focus on solutions suitable for advancing towards a truly pervasive and liquid AI, enabling edge devices to accomplish training and inference with the same accuracy of cloud AI, without harming our environment. For doing so, highly efficient learning methods will be investigated including model compression, reservoir and neuromorphic computing, and distributed/decentralized and collaborative data/client selection algorithms. Realistic use cases from sustainable development goals will be considered to validate the selected solutions.

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.

The focus of this talk is not about my research in AI or Robotics but my own journey on trying to do research and understand intelligence in a rapidly evolving research landscape. I will trace my path from conducting early-stage research during graduate school, to working on practical solutions within a startup environment, and finally to my current role where I participate in more structured research at a major tech company. Through these varied experiences, I will provide different perspectives on research and talk about how my core beliefs on intelligence have changed and sometimes even been compromised. There are no lessons to be learned from my stories, but hopefully they will be entertaining.

Gaze shifts in humans serve to direct high-resolution vision provided by the fovea towards areas in the environment. Gaze can be considered a proxy for attention or indicator of the relative importance of different parts of the environment. In this talk, we discuss the development of generative models of human gaze in response to visual input. We discuss how such models can be learned, both using supervised learning and using implicit feedback as an agent interacts with the environment, the latter being more plausible in biological agents. We also discuss two ways such models can be used. First, they can be used to improve the performance of artificial autonomous systems, in applications such as autonomous navigation. Second, because these models are contingent on the human’s task, goals, and/or state in the context of the environment, observations of gaze can be used to infer information about user intent. This information can be used to improve human-machine and human robot interaction, by making interfaces more anticipative. We discuss example applications in gaze-typing, robotic tele-operation and human-robot interaction.

High-quality glass lenses are commonplace in the design of optical instrumentation used across the biosciences. However, research-grade glass lenses are often costly, delicate and, depending on the prescription, can involve intricate and lengthy manufacturing - even more so in bioimaging applications. This seminar will outline 3D printing as a viable low-cost alternative for the manufacture of high-performance optical elements, where I will also discuss the creation of the world’s first fully 3D printed microscope and other implementations of 3D printed lenses. Our 3D printed lenses were generated using consumer-grade 3D printers and pose a 225x materials cost-saving compared to glass optics. Moreover, they can be produced in any lab or home environment and offer great potential for education and outreach. Following performance validation, our 3D printed optics were implemented in the production of a fully 3D printed microscope and demonstrated in histological imaging applications. We also applied low-cost fabrication methods to exotic lens geometries to enhance resolution and contrast across spatial scales and reveal new biological structures. Across these applications, our findings showed that 3D printed lenses are a viable substitute for commercial glass lenses, with the advantage of being relatively low-cost, accessible, and suitable for use in optical instruments. Combining 3D printed lenses with open-source 3D printed microscope chassis designs opens the doors for low-cost applications for rapid prototyping, low-resource field diagnostics, and the creation of cheap educational tools.

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification. Often, comparison studies focus on the end-result of the learning process by measuring and comparing the similarities in the representations of object categories once they have been formed. However, the process of how these representations emerge—that is, the behavioral changes and intermediate stages observed during the acquisition—is less often directly and empirically compared. In this talk, I'm going to report a detailed investigation of the learning dynamics in human observers and various classic and state-of-the-art DNNs. We develop a constrained supervised learning environment to align learning-relevant conditions such as starting point, input modality, available input data and the feedback provided. Across the whole learning process we evaluate and compare how well learned representations can be generalized to previously unseen test data. Comparisons across the entire learning process indicate that DNNs demonstrate a level of data efficiency comparable to human learners, challenging some prevailing assumptions in the field. However, our results also reveal representational differences: while DNNs' learning is characterized by a pronounced generalisation lag, humans appear to immediately acquire generalizable representations without a preliminary phase of learning training set-specific information that is only later transferred to novel data.

Emotion is defined as a rapid psychological process involving experiential, expressive and physiological responses. These emerge following an appraisal process that involves cognitive evaluations of the environment assessing its relevance, implication, coping potential, and normative significance. It has been suggested that changes in appraisal processes lead to changes in the resulting emotional nature. Simultaneously, it was demonstrated that personality can be seen as a predisposition to feel more frequently certain emotions, but the personality-appraisal-emotional response chain is rarely fully investigated. The present project thus sought to investigate the extent to which personality traits influence certain appraisals, which in turn influence the subsequent emotional reactions via a systematic analysis of the link between personality traits of different current models, specific appraisals, and emotional response patterns at the experiential, expressive, and physiological levels. Major results include the coherence of emotion components clustering, and the centrality of the pleasantness, coping potential and consequences appraisals, in context; and the differentiated mediating role of cognitive appraisal in the relation between personality and the intensity and duration of an emotional state, and autonomic arousal, such as Extraversion-pleasantness-experience, and Neuroticism-powerlessness-arousal. Elucidating these relationships deepens our understanding of individual differences in emotional reactivity and spot routes of action on appraisal processes to modify upcoming adverse emotional responses, with a broader societal impact on clinical and non-clinical populations.

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Predictive processing is a computational framework that aims to explain how the brain processes sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In my talk, I will provide an overview of our progress in this endeavor.

Nonstationarity presents a variety of challenges for machine learning systems. One surprising pathology which can arise in nonstationary learning problems is plasticity loss, whereby making progress on new learning objectives becomes more difficult as training progresses. Networks which are unable to adapt in response to changes in their environment experience plateaus or even declines in performance in highly non-stationary domains such as reinforcement learning, where the learner must quickly adapt to new information even after hundreds of millions of optimization steps. The loss of plasticity manifests in a cluster of related empirical phenomena which have been identified by a number of recent works, including the primacy bias, implicit under-parameterization, rank collapse, and capacity loss. While this phenomenon is widely observed, it is still not fully understood. This talk will present exciting recent results which shed light on the mechanisms driving the loss of plasticity in a variety of learning problems and survey methods to maintain network plasticity in non-stationary tasks, with a particular focus on deep reinforcement learning.

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.

Microglia are the brain macrophages, eliciting multifaceted functions to maintain brain homeostasis across lifetime. To achieve this, microglia are able to sense a plethora of signals in their close environment. In the lab, we investigate the effect of nutrients on microglia function for several reasons: 1) Microglia express all the cellular machinery required to sense nutrients; 2) Eating habits have changed considerably over the last century, towards diets rich in fats and sugars; 3) This so-called "Western diet" is accompanied by an increase in the occurrence of neuropathologies, in which microglia are known to play a role. In my talk, I will present data showing how variations in nutrient intake alter microglia function, including exacerbation of synaptic pruning, with profound consequences for neuronal activity and behavior. I will also show unpublished data on the mechanisms underlying the effects of nutrients on microglia, notably through the regulation of their metabolic activity.

Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model describes irrational behaviors depending on the curiosity level. We also proposed a machine learning method to decode temporal curiosity from behavioral data. By applying it to rat behavioral data, we found that the rat had negative curiosity, reflecting conservative selection sticking to more certain options and that the level of curiosity was upregulated by the expected future information obtained from an uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward–curiosity conflicts. Furthermore, it could be effective in diagnosing mental disorders.

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards and the actual rewards. The prominence of the TD theory arises from the observation that firing properties of dopaminergic neurons in the ventral tegmental area appear similar to those of RPE model-neurons in TD learning. Previous implementations of TD learning assume a fixed temporal basis for each stimulus that might eventually predict a reward. Here we show that such a fixed temporal basis is implausible and that certain predictions of TD learning are inconsistent with experiments. We propose instead an alternative theoretical framework, coined FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, feature specific representations of time are learned, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. In FLEX dopamine acts as an instructive signal which helps build temporal models of the environment. FLEX is a general theoretical framework that has many possible biophysical implementations. In order to show that FLEX is a feasible approach, we present a specific biophysically plausible model which implements the principles of FLEX. We show that this implementation can account for various reinforcement learning paradigms, and that its results and predictions are consistent with a preponderance of both existing and reanalyzed experimental data.

Mobile organisms must be capable of deciding both where and when to move in order to keep up with a changing environment; therefore, a strong sense of time is necessary, otherwise, we would fail in many of our movement goals. Despite this intrinsic link between movement and timing, only recently has research begun to investigate the interaction. Two primary effects that have been observed include: movements biasing time estimates (i.e., affecting accuracy) as well as making time estimates more precise. The goal of this presentation is to review this literature, discuss a Bayesian cue combination framework to explain these effects, and discuss the experiments I have conducted to test the framework. The experiments herein include: a motor timing task comparing the effects of movement vs non-movement with and without feedback (Exp. 1A & 1B), a transcranial magnetic stimulation (TMS) study on the role of the supplementary motor area (SMA) in transforming temporal information (Exp. 2), and a perceptual timing task investigating the effect of noisy movement on time perception with both visual and auditory modalities (Exp. 3A & 3B). Together, the results of these studies support the Bayesian cue combination framework, in that: movement improves the precision of time perception not only in perceptual timing tasks but also motor timing tasks (Exp. 1A & 1B), stimulating the SMA appears to disrupt the transformation of temporal information (Exp. 2), and when movement becomes unreliable or noisy there is no longer an improvement in precision of time perception (Exp. 3A & 3B). Although there is support for the proposed framework, more studies (i.e., fMRI, TMS, EEG, etc.) need to be conducted in order to better understand where and how this may be instantiated in the brain; however, this work provides a starting point to better understanding the intrinsic connection between time and movement

Theories from reinforcement learning have been highly influential for interpreting neural activity in the biological circuits critical for animal and human learning. Central among these is the identification of phasic activity in dopamine neurons as a reward prediction error signal that drives learning in basal ganglia and prefrontal circuits. However, recent findings suggest that dopaminergic prediction error signals have access to complex, structured reward predictions and are sensitive to more properties of outcomes than learning theories with simple scalar value predictions might suggest. Here, I will present recent work in which we probed the identity-specific structure of reward prediction errors in an odor-guided choice task and found evidence for multiple predictive “threads” that segregate reward predictions, and reward prediction errors, according to the specific sensory features of anticipated outcomes. Our results point to an expanded class of neural reinforcement learning algorithms in which biological agents learn rich associative structure from their environment and leverage it to build reward predictions that include information about the specific, and perhaps idiosyncratic, features of available outcomes, using these to guide behavior in even quite simple reward learning tasks.

Zoe has developed an open-source tool PhaSER, which allows her to perform real-time oscillatory phase estimation and apply optogenetic manipulations at precise phases of hippocampal theta during high-density electrophysiological recordings in head-fixed mice while they navigate a virtual environment. The precise timing of single-unit spiking relative to network-wide oscillations (i.e., phase locking) has long been thought to maintain excitatory-inhibitory homeostasis and coordinate cognitive processes, but due to intense experimental demands, the causal influence of this phenomenon has never been determined. Thus, we developed PhaSER (Phase-locked Stimulation to Endogenous Rhythms), a tool which allows the user to explore the temporal relationship between single-unit spiking and ongoing oscillatory activity.

Young children have sophisticated representations of their visual and linguistic environment. Where do these representations come from? How much knowledge arises through generic learning mechanisms applied to sensory data, and how much requires more substantive (possibly innate) inductive biases? We examine these questions by training neural networks solely on longitudinal data collected from a single child (Sullivan et al., 2020), consisting of egocentric video and audio streams. Our principal findings are as follows: 1) Based on visual only training, neural networks can acquire high-level visual features that are broadly useful across categorization and segmentation tasks. 2) Based on language only training, networks can acquire meaningful clusters of words and sentence-level syntactic sensitivity. 3) Based on paired visual and language training, networks can acquire word-referent mappings from tens of noisy examples and align their multi-modal conceptual systems. Taken together, our results show how sophisticated visual and linguistic representations can arise through data-driven learning applied to one child’s first-person experience.

The sense of agency refers to the subjective feeling of controlling one's own behavior and, through them, external events. Why is this subjective feeling important for humans? Is it just a by-product of our actions? Previous studies have shown that the sense of agency can affect the intensity of sensory input because we predict the input from our motor intention. However, my research has found that the sense of agency plays more roles than just predictions. It enhances perceptual processes of sensory input and potentially helps to harvest more information about the link between the external world and the self. Furthermore, our recent research found both indirect and direct evidence that the sense of agency is important for people's exploratory behaviors, and this may be linked to proximal exploitations of one's control in the environment. In this talk, I will also introduce the paradigms we use to study the sense of agency as a result of perceptual processes, and our findings of individual differences in this sense and the implications.

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

What do nonhuman primates know about each other and their social environment, how do they allocate their attention, and what are the functional consequences of social decisions in natural settings? Addressing these questions is crucial to hone in on the co-evolution of cognition, social behaviour and communication, and ultimately the evolution of intelligence in the primate order. I will present results from field experimental and observational studies on free-ranging baboons, which tap into the cognitive abilities of these animals. Baboons are particularly valuable in this context as different species reveal substantial variation in social organization and degree of despotism. Field experiments revealed considerable variation in the allocation of social attention: while the competitive chacma baboons were highly sensitive to deviations from the social order, the highly tolerant Guinea baboons revealed a confirmation bias. This bias may be a result of the high gregariousness of the species, which puts a premium on ignoring social noise. Variation in despotism clearly impacted the use of signals to regulate social interactions. For instance, male-male interactions in chacma baboons mostly comprised dominance displays, while Guinea baboon males evolved elaborate greeting rituals that serve to confirm group membership and test social bonds. Strikingly, the structure of signal repertoires does not differ substantially between different baboon species. In conclusion, the motivational disposition to engage in affiliation or aggressiveness appears to be more malleable during evolution than structural elements of the behavioral repertoire; this insight is crucial for understanding the dynamics of social evolution.

Epilepsy is one of the most common neurological diseases according to the World Health Organization (WHO) affecting around 70 million people worldwide [WHO]. Patients who suffer from epilepsy also suffer from a variety of neuro-psychiatric co-morbidities, which they can experience as crippling as the seizure condition itself. Adequate organization of cerebral white matter is utterly important for cognitive development. The failure of integration of neurologic function with cognition is reflected in neuro-psychiatric disease, such as autism spectrum disorder (ASD). However, in epilepsy we know little about the importance of white matter abnormalities in epilepsy-associated co-morbidities. Epilepsy surgery is an important therapy strategy in patients where conventional anti-epileptic drug treatment fails . On histology of the resected brain samples, malformations of cortical development (MCD) are common among the epilepsy surgery population, especially focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC). Both pathologies are associated with constitutive activation of the mTOR pathway. Interestingly, some type of FCD is morphological similar to TSC cortical tubers including the abnormalities of the white matter. Hypomyelination with lack of myelin-producing cells, the oligodendrocytes, within the lesional area is a striking phenomenon. Impairment of the complex myelination process can have a major impact on brain function. In the worst case leading to distorted or interrupted neurotransmissions. It is still unclear whether the observed myelin pathology in epilepsy surgical specimens is primarily related to the underlying malformation process or is just a secondary phenomenon of recurrent epileptic seizures creating a toxic micro-environment which hampers myelin formation. Interestingly, mTORC1 has been implicated as key signal for myelination, thus, promoting the maturation of oligodendrocytes . These results, however, remain controversial. Regardless of the underlying pathophysiologic mechanism, alterations of myelin dynamics, depending on their severity, are known to be linked to various kinds of developmental disorders or neuropsychiatric manifestations.

Programmed axon death is a widespread and completely preventable mechanism in injury and disease. Mouse and Drosophila studies define a molecular pathway involving activation of SARM1 NA Dase and its prevention by NAD synthesising enzyme NMNAT2 . Loss of axonal NMNAT2 causes its substrate, NMN , to accumulate and activate SARM1 , driving loss of NAD and changes in ATP , ROS and calcium. Animal models caused by genetic mutation, toxins, viruses or metabolic defects can be alleviated by blocking programmed axon death, for example models of CMT1B , chemotherapy-induced peripheral neuropathy (CIPN), rabies and diabetic peripheral neuropathy (DPN). The perinatal lethality of NMNAT2 null mice is completely rescued, restoring a normal, healthy lifespan. Animal models lack the genetic and environmental diversity present in human populations and this is problematic for modelling gene-environment combinations, for example in CIPN and DPN , and identifying rare, pathogenic mutations. Instead, by testing human gene variants in WGS datasets for loss- and gain-of-function, we identified enrichment of rare SARM1 gain-of-function variants in sporadic ALS , despite previous negative findings in SOD1 transgenic mice. We have shown in mice that heterozygous SARM1 loss-of-function is protective from a range of axonal stresses and that naturally-occurring SARM1 loss-of-function alleles are present in human populations. This enables new approaches to identify disorders where blocking SARM1 may be therapeutically useful, and the existence of two dominant negative human variants in healthy adults is some of the best evidence available that drugs blocking SARM1 are likely to be safe. Further loss- and gain-of-function variants in SARM1 and NMNAT2 are being identified and used to extend and strengthen the evidence of association with neurological disorders. We aim to identify diseases, and specific patients, in whom SARM1 -blocking drugs are most likely to be effective.

Since Darwin, comparative research has shown that most animals share basic timing capacities, such as the ability to process temporal regularities and produce rhythmic behaviors. What seems to be more exclusive, however, are the capacities to generate temporal predictions and to display anticipatory behavior at salient time points. These abilities are associated with subcortical structures like basal ganglia (BG) and cerebellum (CE), which are more developed in humans as compared to nonhuman animals. In the first research line, we investigated the basic capacities to extract temporal regularities from the acoustic environment and produce temporal predictions. We did so by adopting a comparative and translational approach, thus making use of a unique EEG dataset including 2 macaque monkeys, 20 healthy young, 11 healthy old participants and 22 stroke patients, 11 with focal lesions in the BG and 11 in the CE. In the second research line, we holistically explore the functional relevance of body-brain physiological interactions in human behavior. Thus, a series of planned studies investigate the functional mechanisms by which body signals (e.g., respiratory and cardiac rhythms) interact with and modulate neurocognitive functions from rest and sleep states to action and perception. This project supports the effort towards individual profiling: are individuals’ timing capacities (e.g., rhythm perception and production), and general behavior (e.g., individual walking and speaking rates) influenced / shaped by body-brain interactions?

To maintain a stable and clear image of the world, our eyes reflexively follow the direction in which a visual scene is moving. Such gaze stabilization mechanisms reduce image blur as we move in the environment. In non-primate mammals, this behavior is initiated by ON-type direction-selective ganglion cells (ON-DSGCs), which detect the direction of image motion and transmit signals to brainstem nuclei that drive compensatory eye movements. However, ON-DSGCs have not yet been functionally identified in primates, raising the possibility that the visual inputs that drive this behavior instead arise in the cortex. In this talk, I will present molecular, morphological and functional evidence for identification of an ON-DSGC in macaque retina. The presence of ON-DSGCs highlights the need to examine the contribution of subcortical retinal mechanisms to normal and aberrant gaze stabilization in the developing and mature visual system. More generally, our findings demonstrate the power of a multimodal approach to study sparsely represented primate RGC types.

Over the course of evolution, the human race has achieved a number of remarkable innovations, that have enabled us to adapt to and benefit from the environment ever more effectively. The ongoing environmental threats and health disasters of our world have now made it crucial to understand the cognitive mechanisms behind innovative behaviours. In my talk, I will present two research projects with examples of innovation-based behavioural adaptation from the taxonomic kingdom of animals, serving as a comparative psychological model for mapping the evolution of innovation. The first project focuses on the challenge of overcoming physical disability. In this study, we investigated an injured kea (Nestor notabilis) that exhibits an efficient, intentional, and innovative tool-use behaviour to compensate his disability, showing evidence for innovation-based adaptation to a physical disability in a non-human species. The second project focuses on the evolution of fire use from a cognitive perspective. Fire has been one of the most dominant ecological forces in human evolution; however, it is still unknown what capabilities and environmental factors could have led to the emergence of fire use. In the core study of this project, we investigated a captive population of Japanese macaques (Macaca fuscata) that has been regularly exposed to campfires during the cold winter months for over 60 years. Our results suggest that macaques are able to take advantage of the positive effects of fire while avoiding the dangers of flames and hot ashes, and exhibit calm behaviour around the bonfire. In addition, I will present a research proposal targeting the foraging behaviour of predatory birds in parts of Australia frequently affected by bushfires. Anecdotal reports suggest that some birds use burning sticks to spread the flames, a behaviour that has not been scientifically observed and evaluated. In summary, the two projects explore innovative behaviours along three different species groups, three different habitats, and three different ecological drivers, providing insights into the cognitive and behavioural mechanisms of adaptation through innovation.

The goal of neuroscience is to understand how the nervous system controls behaviour, not only in the simplified environments of the lab, but also in the natural environments for which nervous systems evolved. In pursuing this goal, neuroscience research is supported by an ever-larger toolbox, ranging from optogenetics to connectomics. However, often these tools are coupled with reductionist approaches for linking nervous systems and behaviour. This course will introduce advanced techniques for measuring and analysing behaviour, as well as three fundamental principles as necessary to understanding biological behaviour: (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies [1]. [1] Gomez-Marin, A., & Ghazanfar, A. A. (2019). The life of behavior. Neuron, 104(1), 25-36