sensorimotor control, mouvement, touch, EEG

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand movements when in contact with a surface, we predicted poorer performance when the participants traced with their bare finger compared to when their tactile sensation was dampened by a smooth, rigid finger splint. The results supported this prediction. EEG source analyses revealed smaller current in the source-localized somatosensory cortex during sensory conflict when the finger directly touched the surface. This finding supports the hypothesis that, in response to mirror-reversed visual feedback, the central nervous system selectively gated task-irrelevant somatosensory inputs, thereby mitigating, though not entirely resolving, the visuo-somatosensory conflict. Together, our results emphasize touch’s involvement in movement control over a surface, challenging the notion that vision predominantly governs goal-directed hand or finger movements.

Consciousness at the edge of chaos

Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

Introduction to protocols.io: Scientific collaboration through open protocols

Research articles and laboratory protocol organization often lack detailed instructions for replicating experiments. protocols.io is an open-access platform where researchers collaboratively create dynamic, interactive, step-by-step protocols that can be executed on mobile devices or the web. Researchers can easily and efficiently share protocols with colleagues, collaborators, the scientific community, or make them public. Real-time communication and interaction keep protocols up to date. Public protocols receive a DOI and enable open communication with authors and researchers to foster efficient experimentation and reproducibility.

Top-down control of neocortical threat memory

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.

Introduction to protocols.io: Scientific collaboration through open protocols

Research articles and laboratory protocol organization often lack detailed instructions for replicating experiments. protocols.io is an open-access platform where researchers collaboratively create dynamic, interactive, step-by-step protocols that can be executed on mobile devices or the web. Researchers can easily and efficiently share protocols with colleagues, collaborators, the scientific community, or make them public. Real-time communication and interaction keep protocols up to date. Public protocols receive a DOI and enable open communication with authors and researchers to foster efficient experimentation and reproducibility.

The SIMple microscope: Development of a fibre-based platform for accessible SIM imaging in unconventional environments

Advancements in imaging speed, depth and resolution have made structured illumination microscopy (SIM) an increasingly powerful optical sectioning (OS) and super-resolution (SR) technique, but these developments remain inaccessible to many life science researchers due to the cost, optical complexity and delicacy of these instruments. We address these limitations by redesigning the optical path using in-line fibre components that are compact, lightweight and easily assembled in a “Plug & Play” modality, without compromising imaging performance. They can be integrated into an existing widefield microscope with a minimum of optical components and alignment, making OS-SIM more accessible to researchers with less optics experience. We also demonstrate a complete SR-SIM imaging system with dimensions 300 mm × 300 mm × 450 mm. We propose to enable accessible SIM imaging by utilising its compact, lightweight and robust design to transport it where it is needed, and image in “unconventional” environments where factors such as temperature and biosafety considerations currently limit imaging experiments.

OpenNeuro FitLins GLM: An Accessible, Semi-Automated Pipeline for OpenNeuro Task fMRI Analysis

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Digital Traces of Human Behaviour: From Political Mobilisation to Conspiracy Narratives

Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspiracy narratives reveals how digital traces illuminate psychological mechanisms of belief and community formation. Longitudinal analysis of YouTube conspiracy content demonstrates how narratives systematically address existential, epistemic, and social needs, while examination of alt-tech platforms shows how emotions of anger, contempt, and disgust correlate with violence-legitimating discourse, with significant differences between narratives associated with offline violence versus peaceful communities. This work establishes digital traces as both methodological innovation and theoretical lens, demonstrating that computational social science can illuminate fundamental questions about polarisation, mobilisation, and collective behaviour across contexts from electoral politics to conspiracy communities.

Neural circuits underlying sleep structure and functions

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM) sleep and emotion encoding to improve treatments for mental health. This lecture will summarize our investigation of the cellular and circuit mechanisms underlying sleep architecture, sleep oscillations, and local brain dynamics across sleep-wake states using electrophysiological recordings combined with single-cell calcium imaging or optogenetics. The presentation will detail the discovery of a 'somato-dendritic decoupling'in prefrontal cortex pyramidal neurons underlying REM sleep-dependent stabilization of optimal emotional memory traces. This decoupling reflects a tonic inhibition at the somas of pyramidal cells, occurring simultaneously with a selective disinhibition of their dendritic arbors selectively during REM sleep. Recent findings on REM sleep-dependent subcortical inputs and neuromodulation of this decoupling will be discussed in the context of synaptic plasticity and the optimization of emotional responses in the maintenance of mental health.

From Spiking Predictive Coding to Learning Abstract Object Representation

In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification performance on challenging benchmarks. A second part of the talk will feature our lab’s efforts to explain how infants and toddlers might learn abstract object representations without supervision. I will present deep learning models that exploit the temporal and multimodal structure of their sensory inputs to learn representations of individual objects, object categories, or abstract super-categories such as „kitchen object“ in a fully unsupervised fashion. These models offer a parsimonious account of how abstract semantic knowledge may be rooted in children's embodied first-person experiences.

The Unconscious Eye: What Involuntary Eye Movements Reveal About Brain Processing

Open Hardware Microfluidics

What’s the point of having scientific and technological innovations when only a few can benefit from them? How can we make science more inclusive? Those questions are always in the back of my mind when we perform research in our laboratory, and we have a strong focus on the scientific accessibility of our developed methods from microfabrication to sensor development.

“A Focus on 3D Printed Lenses: Rapid prototyping, low-cost microscopy and enhanced imaging for the life sciences”

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.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Harnessing Big Data in Neuroscience: From Mapping Brain Connectivity to Predicting Traumatic Brain Injury

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

What it’s like is all there is: The value of Consciousness

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations about the putative biological mechanisms through which information processing becomes conscious. This is all good and fine, even promising, but we also seem to have thrown the baby out with the bathwater, or at least to have forgotten it in the crib: consciousness is not just mechanisms, it’s what it feels like. In other words, while we know thousands of informative studies about access-consciousness, we have little in the way of phenomenal consciousness. But that — what it feels like — is truly what “consciousness” is about. Understanding why it feels like something to be me and nothing (panpsychists notwithstanding) for a stone to be a stone is what the field has always been after. However, while it is relatively easy to study access-consciousness through the contrastive approach applied to reports, it is much less clear how to study phenomenology, its structure and its function. Here, I first overview work on what consciousness does (the "how"). Next, I ask what difference feeling things makes and what function phenomenology might play. I argue that subjective experience has intrinsic value and plays a functional role in everything that we do.

Vision for perception versus vision for action: dissociable contributions of visual sensory drives from primary visual cortex and superior colliculus neurons to orienting behaviors

The primary visual cortex (V1) directly projects to the superior colliculus (SC) and is believed to provide sensory drive for eye movements. Consistent with this, a majority of saccade-related SC neurons also exhibit short-latency, stimulus-driven visual responses, which are additionally feature-tuned. However, direct neurophysiological comparisons of the visual response properties of the two anatomically-connected brain areas are surprisingly lacking, especially with respect to active looking behaviors. I will describe a series of experiments characterizing visual response properties in primate V1 and SC neurons, exploring feature dimensions like visual field location, spatial frequency, orientation, contrast, and luminance polarity. The results suggest a substantial, qualitative reformatting of SC visual responses when compared to V1. For example, SC visual response latencies are actively delayed, independent of individual neuron tuning preferences, as a function of increasing spatial frequency, and this phenomenon is directly correlated with saccadic reaction times. Such “coarse-to-fine” rank ordering of SC visual response latencies as a function of spatial frequency is much weaker in V1, suggesting a dissociation of V1 responses from saccade timing. Consistent with this, when we next explored trial-by-trial correlations of individual neurons’ visual response strengths and visual response latencies with saccadic reaction times, we found that most SC neurons exhibited, on a trial-by-trial basis, stronger and earlier visual responses for faster saccadic reaction times. Moreover, these correlations were substantially higher for visual-motor neurons in the intermediate and deep layers than for more superficial visual-only neurons. No such correlations existed systematically in V1. Thus, visual responses in SC and V1 serve fundamentally different roles in active vision: V1 jumpstarts sensing and image analysis, but SC jumpstarts moving. I will finish by demonstrating, using V1 reversible inactivation, that, despite reformatting of signals from V1 to the brainstem, V1 is still a necessary gateway for visually-driven oculomotor responses to occur, even for the most reflexive of eye movement phenomena. This is a fundamental difference from rodent studies demonstrating clear V1-independent processing in afferent visual pathways bypassing the geniculostriate one, and it demonstrates the importance of multi-species comparisons in the study of oculomotor control.

Neural makers of lapses in attention during sustained ‘real-world’ task performance

Lapses in attention are ubiquitous and, unfortunately, the cause of many tragic accidents. One potential solution may be to develop assistance systems which can use objective, physiological signals to monitor attention levels and predict a lapse in attention before it occurs. As it stands, it is unclear which physiological signals are the most reliable markers of inattention, and even less is known about how reliably they will work in a more naturalistic setting. My project aims to address these questions across two experiments: a lab-based experiment and a more ‘real-world’ experiment. In this talk I will present the findings from my lab experiment, in which we combined EEG and pupillometry to detect markers of inattention during two computerised sustained attention tasks. I will then present the methods for my second, more ‘naturalistic’ experiment in which we use the same methods (EEG and pupillometry) to examine whether these markers can still be extracted from noisier data.

Memory formation in hippocampal microcircuit

The centre of memory is the medial temporal lobe (MTL) and especially the hippocampus. In our research, a more flexible brain-inspired computational microcircuit of the CA1 region of the mammalian hippocampus was upgraded and used to examine how information retrieval could be affected under different conditions. Six models (1-6) were created by modulating different excitatory and inhibitory pathways. The results showed that the increase in the strength of the feedforward excitation was the most effective way to recall memories. In other words, that allows the system to access stored memories more accurately.

Where are you Moving? Assessing Precision, Accuracy, and Temporal Dynamics in Multisensory Heading Perception Using Continuous Psychophysics

Contentopic mapping and object dimensionality - a novel understanding on the organization of object knowledge

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.

Rethinking Attention: Dynamic Prioritization

Decades of research on understanding the mechanisms of attentional selection have focused on identifying the units (representations) on which attention operates in order to guide prioritized sensory processing. These attentional units fit neatly to accommodate our understanding of how attention is allocated in a top-down, bottom-up, or historical fashion. In this talk, I will focus on attentional phenomena that are not easily accommodated within current theories of attentional selection – the “attentional platypuses,” as they allude to an observation that within biological taxonomies the platypus does not fit into either mammal or bird categories. Similarly, attentional phenomena that do not fit neatly within current attentional models suggest that current models need to be revised. I list a few instances of the ‘attentional platypuses” and then offer a new approach, the Dynamically Weighted Prioritization, stipulating that multiple factors impinge onto the attentional priority map, each with a corresponding weight. The interaction between factors and their corresponding weights determines the current state of the priority map which subsequently constrains/guides attention allocation. I propose that this new approach should be considered as a supplement to existing models of attention, especially those that emphasize categorical organizations.

On the principle of accentuation in perceptual organization: Visual, cognitive and biological implications

Screen Savers : Protecting adolescent mental health in a digital world

In our rapidly evolving digital world, there is increasing concern about the impact of digital technologies and social media on the mental health of young people. Policymakers and the public are nervous. Psychologists are facing mounting pressures to deliver evidence that can inform policies and practices to safeguard both young people and society at large. However, research progress is slow while technological change is accelerating.My talk will reflect on this, both as a question of psychological science and metascience. Digital companies have designed highly popular environments that differ in important ways from traditional offline spaces. By revisiting the foundations of psychology (e.g. development and cognition) and considering digital changes' impact on theories and findings, we gain deeper insights into questions such as the following. (1) How do digital environments exacerbate developmental vulnerabilities that predispose young people to mental health conditions? (2) How do digital designs interact with cognitive and learning processes, formalised through computational approaches such as reinforcement learning or Bayesian modelling?However, we also need to face deeper questions about what it means to do science about new technologies and the challenge of keeping pace with technological advancements. Therefore, I discuss the concept of ‘fast science’, where, during crises, scientists might lower their standards of evidence to come to conclusions quicker. Might psychologists want to take this approach in the face of technological change and looming concerns? The talk concludes with a discussion of such strategies for 21st-century psychology research in the era of digitalization.

Brain-Wide Compositionality and Learning Dynamics in Biological Agents

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales. Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

Feedback-induced dispositional changes in risk preferences

Contrary to the original normative decision-making standpoint, empirical studies have repeatedly reported that risk preferences are affected by the disclosure of choice outcomes (feedback). Although no consensus has yet emerged regarding the properties and mechanisms of this effect, a widespread and intuitive hypothesis is that repeated feedback affects risk preferences by means of a learning effect, which alters the representation of subjective probabilities. Here, we ran a series of seven experiments (N= 538), tailored to decipher the effects of feedback on risk preferences. Our results indicate that the presence of feedback consistently increases risk-taking, even when the risky option is economically less advantageous. Crucially, risk-taking increases just after the instructions, before participants experience any feedback. These results challenge the learning account, and advocate for a dispositional effect, induced by the mere anticipation of feedback information. Epistemic curiosity and regret avoidance may drive this effect in partial and complete feedback conditions, respectively.

Trackoscope: A low-cost, open, autonomous tracking microscope for long-term observations of microscale organisms

Cells and microorganisms are motile, yet the stationary nature of conventional microscopes impedes comprehensive, long-term behavioral and biomechanical analysis. The limitations are twofold: a narrow focus permits high-resolution imaging but sacrifices the broader context of organism behavior, while a wider focus compromises microscopic detail. This trade-off is especially problematic when investigating rapidly motile ciliates, which often have to be confined to small volumes between coverslips affecting their natural behavior. To address this challenge, we introduce Trackoscope, an 2-axis autonomous tracking microscope designed to follow swimming organisms ranging from 10μm to 2mm across a 325 square centimeter area for extended durations—ranging from hours to days—at high resolution. Utilizing Trackoscope, we captured a diverse array of behaviors, from the air-water swimming locomotion of Amoeba to bacterial hunting dynamics in Actinosphaerium, walking gait in Tardigrada, and binary fission in motile Blepharisma. Trackoscope is a cost-effective solution well-suited for diverse settings, from high school labs to resource-constrained research environments. Its capability to capture diverse behaviors in larger, more realistic ecosystems extends our understanding of the physics of living systems. The low-cost, open architecture democratizes scientific discovery, offering a dynamic window into the lives of previously inaccessible small aquatic organisms.

Face matching and decision making: The influence of framing, task presentation and criterion placement

Many situations rely on the accurate identification of people with whom we are unfamiliar. For example, security at airports or in police investigations require the identification of individuals from photo-ID. Yet, the identification of unfamiliar faces is error prone, even for practitioners who routinely perform this task. Indeed, even training protocols often yield no discernible improvement. The challenge of unfamiliar face identification is often thought of as a perceptual problem; however, this assumption ignores the potential role of decision-making and its contributing factors (e.g., criterion placement). In this talk, I am going to present a series of experiments that investigate the role of decision-making in face identification.

A Breakdown of the Global Open Science Hardware (GOSH) Movement

This seminar, hosted by the LIBRE hub project, will provide an in-depth introduction to the Global Open Science Hardware (GOSH) movement. Since its inception, GOSH has been instrumental in advancing open-source hardware within scientific research, fostering a diverse and active community. The seminar will cover the history of GOSH, its current initiatives, and future opportunities, with a particular focus on the contributions and activities of the Latin American branch. This session aims to inform researchers, educators, and policy-makers about the significance and impact of GOSH in promoting accessibility and collaboration in science instrumentation.

Light-gated membrane channels: Discovery and creation of diversity, principles from protein structure, and cell-function access to biology

Open source FPGA tools for building research devices

Edmund will present why to use FPGAs when building scientific instruments, when and why to use open source FPGA tools, the history of their development, their development status, currently supported FPGA families and functions, current developments in design languages and tools, the community, freely available design blocks, and possible future developments.

Toward globally accessible neuroimaging: Building the OSI2ONE MRI Scanner in Paraguay

The Open Source Imaging Initiative has recently released a fully open source low field MRI scanner called the OSI2ONE. We are currently building this system at the Universidad Paraguayo Alemana in Asuncion, Paraguay for a neuroimaging project at a clinic in Bolivia. I will discuss the process of construction, important considerations before you build, and future work planned with this device.

Exploring the cerebral mechanisms of acoustically-challenging speech comprehension - successes, failures and hope

Comprehending speech under acoustically challenging conditions is an everyday task that we can often execute with ease. However, accomplishing this requires the engagement of cognitive resources, such as auditory attention and working memory. The mechanisms that contribute to the robustness of speech comprehension are of substantial interest in the context of hearing mild to moderate hearing impairment, in which affected individuals typically report specific difficulties in understanding speech in background noise. Although hearing aids can help to mitigate this, they do not represent a universal solution, thus, finding alternative interventions is necessary. Given that age-related hearing loss (“presbycusis”) is inevitable, developing new approaches is all the more important in the context of aging populations. Moreover, untreated hearing loss in middle age has been identified as the most significant potentially modifiable predictor of dementia in later life. I will present research that has used a multi-methodological approach (fMRI, EEG, MEG and non-invasive brain stimulation) to try to elucidate the mechanisms that comprise the cognitive “last mile” in speech acousticallychallenging speech comprehension and to find ways to enhance them.

Modelling the fruit fly brain and body

Through recent advances in microscopy, we now have an unprecedented view of the brain and body of the fruit fly Drosophila melanogaster. We now know the connectivity at single neuron resolution across the whole brain. How do we translate these new measurements into a deeper understanding of how the brain processes sensory information and produces behavior? I will describe two computational efforts to model the brain and the body of the fruit fly. First, I will describe a new modeling method which makes highly accurate predictions of neural activity in the fly visual system as measured in the living brain, using only measurements of its connectivity from a dead brain [1], joint work with Jakob Macke. Second, I will describe a whole body physics simulation of the fruit fly which can accurately reproduce its locomotion behaviors, both flight and walking [2], joint work with Google DeepMind.

Combined electrophysiological and optical recording of multi-scale neural circuit dynamics

This webinar will showcase new approaches for electrophysiological recordings using our silicon neural probes and surface arrays combined with diverse optical methods such as wide-field or 2-photon imaging, fiber photometry, and optogenetic perturbations in awake, behaving mice. Multi-modal recording of single units and local field potentials across cortex, hippocampus and thalamus alongside calcium activity via GCaMP6F in cortical neurons in triple-transgenic animals or in hippocampal astrocytes via viral transduction are brought to bear to reveal hitherto inaccessible and under-appreciated aspects of coordinated dynamics in the brain.

Enabling witnesses to actively explore faces and reinstate study-test pose during a lineup increases discrimination accuracy

In 2014, the US National Research Council called for the development of new lineup technologies to increase eyewitness identification accuracy (National Research Council, 2014). In a police lineup, a suspect is presented alongside multiple individuals known to be innocent who resemble the suspect in physical appearance know as fillers. A correct identification decision by an eyewitness can lead to a guilty suspect being convicted or an innocent suspect being exonerated from suspicion. An incorrect decision can result in the perpetrator remaining at large, or even a wrongful conviction of a mistakenly identified person. Incorrect decisions carry considerable human and financial costs, so it is essential to develop and enact lineup procedures that maximise discrimination accuracy, or the witness’ ability to distinguish guilty from innocent suspects. This talk focuses on new technology and innovation in the field of eyewitness identification. We will focus on the interactive lineup, which is a procedure that we developed based on research and theory from the basic science literature on face perception and recognition. The interactive lineup enables witnesses to actively explore and dynamically view the lineup members. The procedure has been shown to maximize discrimination accuracy, which is the witness’ ability to discriminate guilty from innocent suspects. The talk will conclude by reflecting on emerging technological frontiers and research opportunities.

Perception in Autism: Testing Recent Bayesian Inference Accounts

Distinctive features of experiential time: Duration, speed and event density

William James’s use of “time in passing” and “stream of thoughts” may be two sides of the same coin that emerge from the brain segmenting the continuous flow of information into discrete events. Departing from that idea, we investigated how the content of a realistic scene impacts two distinct temporal experiences: the felt duration and the speed of the passage of time. I will present you the results from an online study in which we used a well-established experimental paradigm, the temporal bisection task, which we extended to passage of time judgments. 164 participants classified seconds-long videos of naturalistic scenes as short or long (duration), or slow or fast (passage of time). Videos contained a varying number and type of events. We found that a large number of events lengthened subjective duration and accelerated the felt passage of time. Surprisingly, participants were also faster at estimating their felt passage of time compared to duration. The perception of duration heavily depended on objective duration, whereas the felt passage of time scaled with the rate of change. Altogether, our results support a possible dissociation of the mechanisms underlying the two temporal experiences.

Time perception in film viewing as a function of film editing

Filmmakers and editors have empirically developed techniques to ensure the spatiotemporal continuity of a film's narration. In terms of time, editing techniques (e.g., elliptical, overlapping, or cut minimization) allow for the manipulation of the perceived duration of events as they unfold on screen. More specifically, a scene can be edited to be time compressed, expanded, or real-time in terms of its perceived duration. Despite the consistent application of these techniques in filmmaking, their perceptual outcomes have not been experimentally validated. Given that viewing a film is experienced as a precise simulation of the physical world, the use of cinematic material to examine aspects of time perception allows for experimentation with high ecological validity, while filmmakers gain more insight on how empirically developed techniques influence viewers' time percept. Here, we investigated how such time manipulation techniques of an action affect a scene's perceived duration. Specifically, we presented videos depicting different actions (e.g., a woman talking on the phone), edited according to the techniques applied for temporal manipulation and asked participants to make verbal estimations of the presented scenes' perceived durations. Analysis of data revealed that the duration of expanded scenes was significantly overestimated as compared to that of compressed and real-time scenes, as was the duration of real-time scenes as compared to that of compressed scenes. Therefore, our results validate the empirical techniques applied for the modulation of a scene's perceived duration. We also found interactions on time estimates of scene type and editing technique as a function of the characteristics and the action of the scene presented. Thus, these findings add to the discussion that the content and characteristics of a scene, along with the editing technique applied, can also modulate perceived duration. Our findings are discussed by considering current timing frameworks, as well as attentional saliency algorithms measuring the visual saliency of the presented stimuli.

Impact of personality profiles on emotion regulation efficiency: insights on experience, expressivity and physiological arousal

People are confronted every day with internal or external stimuli that can elicit emotions. In order to avoid negative ones, or to pursue individual aims, emotions are often regulated. The available emotion regulation strategies have been previously described as efficient or inefficient, but many studies highlighted that the strategies’ efficiency may be influenced by some different aspects such as personality. In this project, the efficiency of several strategies (e.g., reappraisal, suppression, distraction, …) has been studied according to personality profiles, by using the Big Five personality model and the Maladaptive Personality Trait Model. Moreover, the strategies’ efficiency has been tested according to the main emotional responses, namely experience, expressivity and physiological arousal. Results mainly highlighted the differential impact of strategies on individuals and a slight impact of personality. An important factor seems however to be the emotion parameter we are considering, potentially revealing a complex interplay between strategy, personality, and the considered emotion response. Based on these outcomes, further clinical aspects and recommendations will be also discussed.

Deepfake Detection in Super-Recognizers and Police Officers

Using videos from the Deepfake Detection Challenge (cf. Groh et al., 2021), we investigated human deepfake detection performance (DDP) in two unique observer groups: Super-Recognizers (SRs) and "normal" officers from within the 18K members of the Berlin Police. SRs were identified either via previously proposed lab-based procedures (Ramon, 2021) or the only existing tool for SR identification involving increasingly challenging, authentic forensic material: beSure® (Berlin Test For Super-Recognizer Identification; Ramon & Rjosk, 2022). Across two experiments we examined deepfake detection performance (DDP) in participants who judged single videos and pairs of videos in a 2AFC decision setting. We explored speed-accuracy trade-offs in DDP, compared DDP between lab-identified SRs and non-SRs, and police officers whose face identity processing skills had been extensively tested using challenging. In this talk I will discuss our surprising findings and argue that further work is needed too determine whether face identity processing is related to DDP or not.

The Role of Spatial and Contextual Relations of real world objects in Interval Timing

In the real world, object arrangement follows a number of rules. Some of the rules pertain to the spatial relations between objects and scenes (i.e., syntactic rules) and others about the contextual relations (i.e., semantic rules). Research has shown that violation of semantic rules influences interval timing with the duration of scenes containing such violations to be overestimated as compared to scenes with no violations. However, no study has yet investigated whether both semantic and syntactic violations can affect timing in the same way. Furthermore, it is unclear whether the effect of scene violations on timing is due to attentional or other cognitive accounts. Using an oddball paradigm and real-world scenes with or without semantic and syntactic violations, we conducted two experiments on whether time dilation will be obtained in the presence of any type of scene violation and the role of attention in any such effect. Our results from Experiment 1 showed that time dilation indeed occurred in the presence of syntactic violations, while time compression was observed for semantic violations. In Experiment 2, we further investigated whether these estimations were driven by attentional accounts, by utilizing a contrast manipulation of the target objects. The results showed that an increased contrast led to duration overestimation for both semantic and syntactic oddballs. Together, our results indicate that scene violations differentially affect timing due to violation processing differences and, moreover, their effect on timing seems to be sensitive to attentional manipulations such as target contrast.

Using Adversarial Collaboration to Harness Collective Intelligence

There are many mysteries in the universe. One of the most significant, often considered the final frontier in science, is understanding how our subjective experience, or consciousness, emerges from the collective action of neurons in biological systems. While substantial progress has been made over the past decades, a unified and widely accepted explanation of the neural mechanisms underpinning consciousness remains elusive. The field is rife with theories that frequently provide contradictory explanations of the phenomenon. To accelerate progress, we have adopted a new model of science: adversarial collaboration in team science. Our goal is to test theories of consciousness in an adversarial setting. Adversarial collaboration offers a unique way to bolster creativity and rigor in scientific research by merging the expertise of teams with diverse viewpoints. Ideally, we aim to harness collective intelligence, embracing various perspectives, to expedite the uncovering of scientific truths. In this talk, I will highlight the effectiveness (and challenges) of this approach using selected case studies, showcasing its potential to counter biases, challenge traditional viewpoints, and foster innovative thought. Through the joint design of experiments, teams incorporate a competitive aspect, ensuring comprehensive exploration of problems. This method underscores the importance of structured conflict and diversity in propelling scientific advancement and innovation.

Measures and models of multisensory integration in reaction times

First, a new measure of MI for reaction times is proposed that takes the entire RT distribution into account. Second, we present some recent developments in TWIN modeling, including a new proposal for the sound-induced flash illusion (SIFI).

Towards Human Systems Biology of Sleep/Wake Cycles: Phosphorylation Hypothesis of Sleep

The field of human biology faces three major technological challenges. Firstly, the causation problem is difficult to address in humans compared to model animals. Secondly, the complexity problem arises due to the lack of a comprehensive cell atlas for the human body, despite its cellular composition. Lastly, the heterogeneity problem arises from significant variations in both genetic and environmental factors among individuals. To tackle these challenges, we have developed innovative approaches. These include 1) mammalian next-generation genetics, such as Triple CRISPR for knockout (KO) mice and ES mice for knock-in (KI) mice, which enables causation studies without traditional breeding methods; 2) whole-body/brain cell profiling techniques, such as CUBIC, to unravel the complexity of cellular composition; and 3) accurate and user-friendly technologies for measuring sleep and awake states, exemplified by ACCEL, to facilitate the monitoring of fundamental brain states in real-world settings and thus address heterogeneity in human.

Characterising Representations of Goal Obstructiveness and Uncertainty Across Behavior, Physiology, and Brain Activity Through a Video Game Paradigm

The nature of emotions and their neural underpinnings remain debated. Appraisal theories such as the component process model propose that the perception and evaluation of events (appraisal) is the key to eliciting the range of emotions we experience. Here we study whether the framework of appraisal theories provides a clearer account for the differentiation of emotional episodes and their functional organisation in the brain. We developed a stealth game to manipulate appraisals in a systematic yet immersive way. The interactive nature of video games heightens self-relevance through the experience of goal-directed action or reaction, evoking strong emotions. We show that our manipulations led to changes in behaviour, physiology and brain activations.

Bayesian expectation in the perception of the timing of stimulus sequences

In the current virtual journal club Dr Di Luca will present findings from a series of psychophysical investigations where he measured sensitivity and bias in the perception of the timing of stimuli. He will present how improved detection with longer sequences and biases in reporting isochrony can be accounted for by optimal statistical predictions. Among his findings was also that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted to appear more regular. Such change depends on whether the context these sequences are presented is also regular. Dr Di Luca will present a Bayesian model for the combination of dynamically updated expectations, in the form of a priori probability, with incoming sensory information. These findings contribute to the understanding of how the brain processes temporal information to shape perceptual experiences.

Astrocyte reprogramming / activation and brain homeostasis

Astrocytes are multifunctional glial cells, implicated in neurogenesis and synaptogenesis, supporting and fine-tuning neuronal activity and maintaining brain homeostasis by controlling blood-brain barrier permeability. During the last years a number of studies have shown that astrocytes can also be converted into neurons if they force-express neurogenic transcription factors or miRNAs. Direct astrocytic reprogramming to induced-neurons (iNs) is a powerful approach for manipulating cell fate, as it takes advantage of the intrinsic neural stem cell (NSC) potential of brain resident reactive astrocytes. To this end, astrocytic cell fate conversion to iNs has been well-established in vitro and in vivo using combinations of transcription factors (TFs) or chemical cocktails. Challenging the expression of lineage-specific TFs is accompanied by changes in the expression of miRNAs, that post-transcriptionally modulate high numbers of neurogenesis-promoting factors and have therefore been introduced, supplementary or alternatively to TFs, to instruct direct neuronal reprogramming. The neurogenic miRNA miR-124 has been employed in direct reprogramming protocols supplementary to neurogenic TFs and other miRNAs to enhance direct neurogenic conversion by suppressing multiple non-neuronal targets. In our group we aimed to investigate whether miR-124 is sufficient to drive direct reprogramming of astrocytes to induced-neurons (iNs) on its own both in vitro and in vivo and elucidate its independent mechanism of reprogramming action. Our in vitro data indicate that miR-124 is a potent driver of the reprogramming switch of astrocytes towards an immature neuronal fate. Elucidation of the molecular pathways being triggered by miR-124 by RNA-seq analysis revealed that miR-124 is sufficient to instruct reprogramming of cortical astrocytes to immature induced-neurons (iNs) in vitro by down-regulating genes with important regulatory roles in astrocytic function. Among these, the RNA binding protein Zfp36l1, implicated in ARE-mediated mRNA decay, was found to be a direct target of miR-124, that be its turn targets neuronal-specific proteins participating in cortical development, which get de-repressed in miR-124-iNs. Furthermore, miR-124 is potent to guide direct neuronal reprogramming of reactive astrocytes to iNs of cortical identity following cortical trauma, a novel finding confirming its robust reprogramming action within the cortical microenvironment under neuroinflammatory conditions. In parallel to their reprogramming properties, astrocytes also participate in the maintenance of blood-brain barrier integrity, which ensures the physiological functioning of the central nervous system and gets affected contributing to the pathology of several neurodegenerative diseases. To study in real time the dynamic physical interactions of astrocytes with brain vasculature under homeostatic and pathological conditions, we performed 2-photon brain intravital imaging in a mouse model of systemic neuroinflammation, known to trigger astrogliosis and microgliosis and to evoke changes in astrocytic contact with brain vasculature. Our in vivo findings indicate that following neuroinflammation the endfeet of activated perivascular astrocytes lose their close proximity and physiological cross-talk with vasculature, however this event is at compensated by the cross-talk of astrocytes with activated microglia, safeguarding blood vessel coverage and maintenance of blood-brain integrity.

Sensory Consequences of Visual Actions

We use rapid eye, head, and body movements to extract information from a new part of the visual scene upon each new gaze fixation. But the consequences of such visual actions go beyond their intended sensory outcomes. On the one hand, intrinsic consequences accompany movement preparation as covert internal processes (e.g., predictive changes in the deployment of visual attention). On the other hand, visual actions have incidental consequences, side effects of moving the sensory surface to its intended goal (e.g., global motion of the retinal image during saccades). In this talk, I will present studies in which we investigated intrinsic and incidental sensory consequences of visual actions and their sensorimotor functions. Our results provide insights into continuously interacting top-down and bottom-up sensory processes, and they reify the necessity to study perception in connection to motor behavior that shapes its fundamental processes.

Current and future trends in neuroimaging

With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.

Connectome-based models of neurodegenerative disease

Neurodegenerative diseases involve accumulation of aberrant proteins in the brain, leading to brain damage and progressive cognitive and behavioral dysfunction. Many gaps exist in our understanding of how these diseases initiate and how they progress through the brain. However, evidence has accumulated supporting the hypothesis that aberrant proteins can be transported using the brain’s intrinsic network architecture — in other words, using the brain’s natural communication pathways. This theory forms the basis of connectome-based computational models, which combine real human data and theoretical disease mechanisms to simulate the progression of neurodegenerative diseases through the brain. In this talk, I will first review work leading to the development of connectome-based models, and work from my lab and others that have used these models to test hypothetical modes of disease progression. Second, I will discuss the future and potential of connectome-based models to achieve clinically useful individual-level predictions, as well as to generate novel biological insights into disease progression. Along the way, I will highlight recent work by my lab and others that is already moving the needle toward these lofty goals.

ALBA webinar series - Breaking down the ivory tower: Ep. 4 Maria José Diógenes

With this webinar series, the ALBA Disability & Accessibility Working Group aims to bring down the ivory tower of ableism among the brain research community, one extraordinary neuroscientist at a time. These webinars give a platform to scientists with disabilities across the globe and neuroscience disciplines, while reflecting on how to promote inclusive working environments and accessibility to research. For this 4th episode, Dr. Maria José Diógenes (iMM - ULisboa, PT) will talk about how her personal story changed her professional life: from the pharmacy to the laboratory bench and from ageing to Rett Syndrome.

Modeling the Navigational Circuitry of the Fly

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.

Event-related frequency adjustment (ERFA): A methodology for investigating neural entrainment

Neural entrainment has become a phenomenon of exceptional interest to neuroscience, given its involvement in rhythm perception, production, and overt synchronized behavior. Yet, traditional methods fail to quantify neural entrainment due to a misalignment with its fundamental definition (e.g., see Novembre and Iannetti, 2018; Rajandran and Schupp, 2019). The definition of entrainment assumes that endogenous oscillatory brain activity undergoes dynamic frequency adjustments to synchronize with environmental rhythms (Lakatos et al., 2019). Following this definition, we recently developed a method sensitive to this process. Our aim was to isolate from the electroencephalographic (EEG) signal an oscillatory component that is attuned to the frequency of a rhythmic stimulation, hypothesizing that the oscillation would adaptively speed up and slow down to achieve stable synchronization over time. To induce and measure these adaptive changes in a controlled fashion, we developed the event-related frequency adjustment (ERFA) paradigm (Rosso et al., 2023). A total of twenty healthy participants took part in our study. They were instructed to tap their finger synchronously with an isochronous auditory metronome, which was unpredictably perturbed by phase-shifts and tempo-changes in both positive and negative directions across different experimental conditions. EEG was recorded during the task, and ERFA responses were quantified as changes in instantaneous frequency of the entrained component. Our results indicate that ERFAs track the stimulus dynamics in accordance with the perturbation type and direction, preferentially for a sensorimotor component. The clear and consistent patterns confirm that our method is sensitive to the process of frequency adjustment that defines neural entrainment. In this Virtual Journal Club, the discussion of our findings will be complemented by methodological insights beneficial to researchers in the fields of rhythm perception and production, as well as timing in general. We discuss the dos and don’ts of using instantaneous frequency to quantify oscillatory dynamics, the advantages of adopting a multivariate approach to source separation, the robustness against the confounder of responses evoked by periodic stimulation, and provide an overview of domains and concrete examples where the methodological framework can be applied.

Neural Mechanisms of Subsecond Temporal Encoding in Primary Visual Cortex

Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.

Consciousness in the cradle: on the emergence of infant experience

Although each of us was once a baby, infant consciousness remains mysterious and there is no received view about when, and in what form, consciousness first emerges. Some theorists defend a ‘late-onset’ view, suggesting that consciousness requires cognitive capacities which are unlikely to be in place before the child’s first birthday at the very earliest. Other theorists defend an ‘early-onset’ account, suggesting that consciousness is likely to be in place at birth (or shortly after) and may even arise during the third trimester. Progress in this field has been difficult, not just because of the challenges associated with procuring the relevant behavioral and neural data, but also because of uncertainty about how best to study consciousness in the absence of the capacity for verbal report or intentional behavior. This review examines both the empirical and methodological progress in this field, arguing that recent research points in favor of early-onset accounts of the emergence of consciousness.

Perceptions of responsiveness and rejection in romantic relationships. What are the implications for individuals and relationship functioning?

From birth, human beings need to be embedded into social ties to function best, because other individuals can provide us with a sense of belonging, which is a fundamental human need. One of the closest bonds we build throughout our life is with our intimate partners. When the relationship involves intimacy and when both partners accept and support each other’s needs and goals (through perceived responsiveness) individuals experience an increase in relationship satisfaction as well as physical and mental well-being. However, feeling rejected by a partner may impair the feeling of connectedness and belonging, and affect emotional and behavioural responses. When we perceive our partner to be responsive to our needs or desires, in turn we naturally strive to respond positively and adequately to our partner’s needs and desires. This implies that individuals are interdependent, and changes in one partner prompt changes in the other. Evidence suggests that partners regulate themselves and co-regulate each other in their emotional, psychological, and physiological responses. However, such processes may threaten the relationship when partners face stressful situations or interactions, like the transition to parenthood or rejection. Therefore, in this presentation, I will provide evidence for the role of perceptions of being accepted or rejected by a significant other on individual and relationship functioning, while considering the contextual settings. The three studies presented here explore romantic relationships, and how perceptions of rejection and responsiveness from the partner impact both individuals, their physiological and their emotional responses, as well as their relationship dynamics.

Great ape interaction: Ladyginian but not Gricean

Non-human great apes inform one another in ways that can seem very humanlike. Especially in the gestural domain, their behavior exhibits many similarities with human communication, meeting widely used empirical criteria for intentionality. At the same time, there remain some manifest differences. How to account for these similarities and differences in a unified way remains a major challenge. This presentation will summarise the arguments developed in a recent paper with Christophe Heintz. We make a key distinction between the expression of intentions (Ladyginian) and the expression of specifically informative intentions (Gricean), and we situate this distinction within a ‘special case of’ framework for classifying different modes of attention manipulation. The paper also argues that the attested tendencies of great ape interaction—for instance, to be dyadic rather than triadic, to be about the here-and-now rather than ‘displaced’—are products of its Ladyginian but not Gricean character. I will reinterpret video footage of great ape gesture as Ladyginian but not Gricean, and distinguish several varieties of meaning that are continuous with one another. We conclude that the evolutionary origins of linguistic meaning lie in gradual changes in not communication systems as such, but rather in social cognition, and specifically in what modes of attention manipulation are enabled by a species’ cognitive phenotype: first Ladyginian and in turn Gricean. The second of these shifts rendered humans, and only humans, ‘language ready’.

cuBNM: GPU-Accelerated Biophysical Network Modeling

Bernstein Conference 2024

Co-development of accommodation and vergence and quantification of their interaction

Bernstein Conference 2024

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning

Bernstein Conference 2024

OpenEyeSim 2.1: Rendering Depth-of-Field and Chromatic Aberration Faster than Real-Time Simulations of Visual Accommodation

Bernstein Conference 2024

Response variability can accelerate learning in feedforward-recurrent networks

Bernstein Conference 2024

Accurate Engagement of the Drosophila Central-Complex Compass During Head-Fixed Path-Constrained Navigation

COSYNE 2022

An anatomically accurate circuit for short- and long-term motivational learning in fruit flies

COSYNE 2022

A biophysical account of multiplication by a single neuron

COSYNE 2022

Beyond accuracy: robustness and generalization properties of biologically plausible learning rules

COSYNE 2022

A closed-loop emulator that accurately predicts brain-machine interface decoder performance

COSYNE 2022

Confidence-guided waiting as an evidence accumulation process

COSYNE 2022

A cortico-collicular circuit for accurate orientation to shelter during escape

COSYNE 2022

Divisive normalization shapes evidence accumulation during dynamic decision-making

COSYNE 2022

Impact of mitofusin 2 on accumbens-associated behaviors and underlying neurobiological mechanisms

FENS Forum 2024

Identifying changes in behavioral strategy from neural responses during evidence accumulation

COSYNE 2022

Identifying changes in behavioral strategy from neural responses during evidence accumulation

COSYNE 2022

Learning accurate path integration in ring attractor models of the head direction system

COSYNE 2022

Learning accurate path integration in ring attractor models of the head direction system

COSYNE 2022

Model architectures for choice-selective sequences in a navigation-based, evidence-accumulation task

COSYNE 2022

Model architectures for choice-selective sequences in a navigation-based, evidence-accumulation task

COSYNE 2022

Probing neural value computations in the nucleus accumbens dopamine signal

COSYNE 2022

Probing neural value computations in the nucleus accumbens dopamine signal

COSYNE 2022

Saccade preparation does not benefit visual change detection

COSYNE 2022

Saccade preparation does not benefit visual change detection

COSYNE 2022

Soft-actor-critic for model-free reinforcement learning of eye saccade control

COSYNE 2022

Soft-actor-critic for model-free reinforcement learning of eye saccade control

COSYNE 2022

An accessible hippocampal dataset for benchmarking models of cognitive mapping

COSYNE 2023

Approximate inference through active computation accounts for human categorization behavior

COSYNE 2023

Accounting for visual cortex variability with distributed neural activity states

COSYNE 2023

A common neural mechanism mediates microsaccades and covert spatial attention

COSYNE 2023

Distinct mechanisms for evidence accumulation and choice memory explain diverse neuronal dynamics

COSYNE 2023

Dopamine release in the nucleus accumbens during backward conditioning

COSYNE 2023

A generalized Weber’s law reveals behaviorally limiting slow noise in evidence accumulation

COSYNE 2023

Information correlations reduce the accuracy of pioneering normative decision makers

COSYNE 2023

Invertible readouts to improve the dynamical accuracy of neural population models

COSYNE 2023

Recording Multi-Neuronal Activity in Unrestrained Animals with 3D Random-Access 2-Photon Microscopy

COSYNE 2023

The tradeoff between fine-tuning and accurate angular integration in small networks

COSYNE 2023

The accumulation of dendritic extracellular Potassium as in vivo model of epilepsy in CA1 pyramidal neurons

COSYNE 2025

An attractive manifold of retinotopic map in a network model explains presaccadic receptive field remapping

COSYNE 2025

Accelerating bio-plausible spiking simulations on the Graphcore IPU

Bernstein Conference 2024