University of Chicago - Grossman Center for Quantitative Biology and Human Behavior

The Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago seeks outstanding applicants for multiple postdoctoral positions in computational and theoretical neuroscience.

Dr. Alexander Herman

We seek a postdoc to work on an exciting federally funded project examining cognitive effort and flexibility in traumatic brain injury (TBI). This project will use a combination of transcranial alternating current stimulation and computational modeling to improve symptoms of mental fatigue after TBI. Our interdisciplinary, joint psychiatry-neurosurgery lab offers a unique opportunity to learn or improve skills in electrophysiology, non-invasive brain stimulation, neuroeconomics, and computational modeling. The ideal candidate has a background in both engineering/computer science and cognitive neuroscience or a strong willingness to learn one or the other. The position offers the opportunity to gain experience working with patients to collect data, but strong staff support exists for this already. The focus of the post-doc will be on analyzing data and writing papers. See our website at www.hermandarrowlab.com

Prof. Li Zhaoping

The Department of Sensory and Sensorimotor Systems (PI Prof. Li Zhaoping) at the Max Planck Institute for Biological Cybernetics and at the University of Tübingen is currently looking for highly skilled and motivated individuals to work on projects aimed towards understanding visual attentional and perceptual processes using fMRI/MRI, TMS and/or EEG methodologies. The framework and motivation of the projects can be found at: https://www.lizhaoping.org/zhaoping/AGZL_HumanVisual.html. The projects can involve, for example, visual search tasks, stereo vision tasks, visual illusions, and will be discussed during the application process. fMRI/MRI, TMS and/or EEG methodologies can be used in combination with eye tracking, and other related methods as necessary. The postdoc will be working closely with the principal investigator and other members of Zhaoping's team when needed. Responsibilities: • Conduct and participate in research projects such as lab and equipment set up, data collection, data analysis, writing reports and papers, and presenting at scientific conferences. • Participate in routine laboratory operations, such as planning and preparations for experiments, lab maintenance and lab procedures. • Coordinate with the PI and other team members for strategies and project planning. • Coordinate with the PI and other team members for project planning, and in supervision of student projects or teaching assistance for university courses in our field. Who we are: We use a multidisciplinary approach to investigate sensory and sensory-motor transforms in the brain (www.lizhaoping.org). Our approaches consist of both theoretical and experimental techniques including human psychophysics, fMRI imaging, electrophysiology and computational modelling. One part of our group is located in the University, in the Centre for Integrative Neurosciences (CIN), and the other part is in the Max Planck Institute for Biological Cybernetics as the Department for Sensory and Sensorimotor Systems. You will have the opportunity to learn other skills in our multidisciplinary group and benefit from interactions with our colleagues in the university as well as internationally. This job opening is for the CIN or the MPI working group. The position (salary level TVöD-Bund E13, 100%) is for a duration of two years, and renewable to additional years. We seek to raise the number of women in research and teaching and therefore urge qualified women to apply. Disabled persons will be preferred in case of equal qualification. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by November 30th, 2022. We look forward to receiving your application that includes (1) a cover letter, including a statement on roughly when you would like to start this position, (2) a motivation statement, (3) a CV, (4) names and contact details of three people for references, (5) if you have them, transcripts from your past and current education listing the courses taken and their grades, (6) if you have them, please also include copies of your degree certificates, (7) you may include a pdf file of your best publication(s), or other documents and information that you think could strengthen your application. Please use pdf files for these documents (and you may combine them into a single pdf file) and send to jobs.li@tuebingen.mpg.de, where also informal inquiries can be addressed. Please note that applications without complete information in (1)-(4) will not be considered, unless the cover letter includes an explanation and/or information about when the needed materials will be supplied. For further opportunities in our group, please visit https://www.lizhaoping.org/jobs.html

Prof. Li Zhaoping

The Department of Sensory and Sensorimotor Systems (PI Prof. Li Zhaoping) at the Max Planck Institute for Biological Cybernetics and at the University of Tübingen is currently looking for highly skilled and motivated individuals to work on projects aimed towards understanding visual attentional and perceptual processes using fMRI/MRI, TMS and/or EEG methodologies. The framework and motivation of the projects can be found at https://www.lizhaoping.org/zhaoping/AGZL_HumanVisual.html. The projects can involve, for example, visual search tasks, stereo vision tasks, visual illusions, and will be discussed during the application process. fMRI/MRI, TMS and/or EEG methodologies can be used in combination with eye tracking, and other related methods as necessary. Responsibilities: • Conduct and participate in research projects such as lab and equipment set up, data collection, data analysis, writing reports and papers, and presenting at scientific conferences. • Participate in routine laboratory operations, such as planning and preparations for experiments, lab maintenance and lab procedures. • Participate in teaching assistance duties for university courses in our field. Who we are: We use a multidisciplinary approach to investigate sensory and sensory-motor transforms in the brain (www.lizhaoping.org). Our approaches consist of both theoretical and experimental techniques including human psychophysics, fMRI imaging, EEG, electrophysiology and computational modelling. One part of our group is located in the University, in the Centre for Integrative Neurosciences (CIN), and the other part is in the Max Planck Institute for Biological Cybernetics as the Department for Sensory and Sensorimotor Systems. You will have the opportunity to learn skills from other members of the group and benefit from multidisciplinary interactions, including with our collaborators locally and internationally. The PhD contract (TVöD-Bund E13, 65%) duration is for 3 years. We seek to raise the number of women in research and teaching and therefore urge qualified women to apply. Disabled persons will be preferred in case of equal qualification. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by November 30th, 2022. We look forward to receiving your application that includes (1) a cover letter, including a statement on roughly when you would like to start this position, (2) a motivation statement, (3) a CV, (4) names and contact details of three people for references, (5) transcripts from your past and current education listing the courses taken and their grades, (6) if you have them, please also include copies of your degree certificates, (7) if you have them, include a pdf file of your best publication(s), or other documents and information that you think could strengthen your application. Please use pdf files for these documents (and you may combine them into a single pdf file) and send to jobs.li@tuebingen.mpg.de, where also informal inquiries can be addressed. Please note that applications without complete information in (1)-(5) will not be considered, unless the cover letter includes an explanation and/or information about when the needed materials will be supplied. For further opportunities in our group, please visit https://www.lizhaoping.org/jobs.html

Prof. Li Zhaoping

The Department for Sensory and Sensorimotor Systems of the Max-Planck-Institute for Biological Cybernetics studies the processing of sensory information (visual, auditory, tactile, olfactory) in the brain and the use of this information for directing body movements and making cognitive decisions. The research is highly interdisciplinary and uses theoretical and experimental approaches in humans. Our methodologies include visual psychophysics, eye tracking, fMRI, EEG, TMS in humans. For more information, please visit the department website: www.lizhaoping.org We are currently looking for a Lab Mechatronics / Programmer/ Research and Admin Assistant (m/f/d) 100% to join us at the next possible opportunity. The position: You will provide hardware, software, and managerial support for a diverse set of brain and neuroscience research activities. This includes: • Computer and IT support of Windows and Linux systems • Programming and debugging of computer code, especially at the stage of setting up new equipment or new experimental platforms • Provide technical, administrative, and operational support in the research data taking process. (The position holder should have the ability to quickly learn the data taking processes involved in the labs.) • Hardware repairs and troubleshooting • Equipment inventory and maintenance • Supervising and training of new equipment users • Setting up, updating and managing the database of knowledge and data from research projects, personnel and activities Our department is interdisciplinary, with research activities including human visual psychophysics, eye tracking, fMRI, EEG, TMS. We are looking for a person with a broad technical knowledge base, who loves working in a scientific environment and who is curious, open-minded, and able to adapt and learn new skills and solve new problems quickly. The set of skills that the individual should either already have or can quickly learn includes: MATLAB/Psychotoolbox, Python/OpenCV, Julia/OpenGL, Java, graphics and display technologies, EEG equipment and similar, eye tracking, optics, electronics/controllers/sensors, Arduino/Raspberry Pi, etc. We offer: We offer highly interesting, challenging and varied tasks; you will work closely and collaboratively with scientists, students, programmers, administrative staff, and central IT and mechanical/electronic workshop support to help achieve the scientific goals of the department. A dedicated team awaits you in an international environment with regular opportunities for further education and training. The salary is paid in accordance with the collective agreement for the public sector (TVöD Bund), based on qualification and experience and will include social security benefits and additional fringe benefits in accordance with public service provisions. This position is initially limited to two years, with the possibility of extensions and a permanent contract. The Max Planck Society seeks to employ more handicapped people and strongly encourages them to apply. Furthermore, we actively support the compatibility of work and family life. The Max Planck Society also seeks to increase the number of women in leadership positions and strongly encourages qualified women to apply. The Max Planck Society strives for gender equality and diversity. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by September 30th, 2022. We look forward to receiving your application that includes a cover letter, your curriculum vitae, relevant certificates, and three names and contacts for reference letters electronically by e-mail to jobs.li@tuebingen.mpg.de, where informal inquiries can also be addressed to. Please note that incomplete applications will not be considered. For further opportunities in our group, please visit http://www.lizhaoping.org/jobs.html

Prof. Pierre Mégevand

The Human Neuron Lab (@LabNeuron), led by Prof. Pierre Mégevand, is dedicated to advancing the detection and prediction of epileptic seizures. The lab also investigates the neuronal basis of human cognitive brain functions. For that purpose, the lab focuses on invasive neurophysiology in the human brain, including ECoG and stereo-EEG. Additionally, unique microelectrode recordings (using Utah arrays and microwire electrodes) give access to the activity of dozens of single neurons in the patient's brain in order to reveal novel markers of epileptic seizures at the neuronal population level. The lab is equipped with state-of-the-art technology for human invasive neurophysiology. It benefits from the powerful computing infrastructure of the University. Importantly, the lab is fully integrated with the epilepsy monitoring unit of Geneva University Hospitals, and thus boasts exceptional access to patients and recordings. This project focuses on defining novel markers of seizures in patients who suffer from epilepsy. Continuous intracranial EEG and microelectrode recordings will be acquired for several weeks. Single-unit activity will be tracked over time for multiple neurons. Activity within the neuronal population will be examined for the presence of patterns that are specific to the patient’s seizures. The performance of seizure detection and prediction using microelectrode recordings will be compared to existing algorithms based on intracranial EEG data. Research tasks: - Acquire, analyze, and curate a uniquely rich dataset of human intracranial EEG and microelectrode recordings - Build a pipeline for semi-automated single-neuron identification and tracking - Establish novel markers of neuronal population activity that identify seizures - Participate in the mapping of sensory, motor and language functions in epilepsy patients - Daily interactions with the patients and staff of the epilepsy monitoring unit Work environment: The University of Geneva is a prestigious research hub in neuroscience, federating many labs that cover the full spectrum from basic to cognitive, translational and clinical research. The neuroscience community in Geneva is also strengthened by rich collaborations with other research institutions, including Campus Biotech, the Wyss Center, and the EPFL. This project is fully funded by a grant from the Swiss National Science Foundation. The PhD and post-doc positions are open for up to 4 years each. Swiss salaries are very attractive in international comparison. The positions will open from May 2021 onwards. Please send your application, including a letter of intent, curriculum vitae, list of publications, and at least two references, by e-mail to: Prof. Pierre Mégevand Division of neurology, Geneva University Hospitals Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland pierre.megevand@unige.ch

Santiago Jaramillo

The Jaramillo lab investigates the neural basis of expectation, attention, decision-making and learning in the context of sound-driven behaviors in mice. Projects during the postdoctoral fellowship will study these cognitive processes by monitoring and manipulating neuronal activity during adaptive behaviors with cell-type and pathway specificity using techniques such as two-photon microscopy (including mesoscope imaging), high-density electrophysiology (using Neuropixels probes), and optogenetic manipulation of neural activity.

Boris Gutkin

A three-year post-doctoral position in theoretical neuroscience is open to explore the mechanisms of interaction between interoceptive cardiac and exteroceptive tactile inputs at the cortical level. We aim to develop and validate a computational model of cardiac and of a somatosensory cortical circuit dynamics in order to determine the conditions under which interactions between exteroceptive and interoceptive inputs occur and which underlying mechanism (e.g., phase-resetting, gating, phasic arousal,..) best explain experimental data. The postdoctoral fellow will be based at the Group for Neural Theory at LNC2, in Boris Gutkin’s team with strong interactions with Catherine Tallon-Baudry’s team. LNC2 is located in the center of Paris within the Cognitive Science Department at Ecole Normale Supérieure, with numerous opportunities to interact with the Paris scientific community at large, in a stimulating and supportive work environment. Group for Neural Theory provides a rich environment and local community for theoretical neuroscience. Lab life is in English, speaking French is not a requirement. Salary according to experience and French rules. Starting date is first semester 2024.

Boris Gutkin, Catherine Tallon-Baudry

A three-year post-doctoral position in theoretical neuroscience is open to explore the mechanisms of interaction between interoceptive cardiac and exteroceptive tactile inputs at the cortical level. We aim to develop data-based computational models of cardiac and somatosensory cortical circuit dynamics. Building on these models we will determine the conditions under which interactions between exteroceptive and interoceptive inputs occur and which underlying mechanisms (e.g., phase-resetting, gating, phasic arousal,..) best explain experimental data.

Organization of thalamic networks and mechanisms of dysfunction in schizophrenia and autism

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

Astrocytes: From Metabolism to Cognition

Different brain cell types exhibit distinct metabolic signatures that link energy economy to cellular function. Astrocytes and neurons, for instance, diverge dramatically in their reliance on glycolysis versus oxidative phosphorylation, underscoring that metabolic fuel efficiency is not uniform across cell types. A key factor shaping this divergence is the structural organization of the mitochondrial respiratory chain into supercomplexes. Specifically, complexes I (CI) and III (CIII) form a CI–CIII supercomplex, but the degree of this assembly varies by cell type. In neurons, CI is predominantly integrated into supercomplexes, resulting in highly efficient mitochondrial respiration and minimal reactive oxygen species (ROS) generation. Conversely, in astrocytes, a larger fraction of CI remains unassembled, freely existing apart from CIII, leading to reduced respiratory efficiency and elevated mitochondrial ROS production. Despite this apparent inefficiency, astrocytes boast a highly adaptable metabolism capable of responding to diverse stressors. Their looser CI–CIII organization allows for flexible ROS signaling, which activates antioxidant programs via transcription factors like Nrf2. This modular architecture enables astrocytes not only to balance energy production but also to support neuronal health and influence complex organismal behaviors.

Neural Representations of Abstract Cognitive Maps in Prefrontal Cortex and Medial Temporal Lobe

FLUXSynID: High-Resolution Synthetic Face Generation for Document and Live Capture Images

Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. In this talk, I will present FLUXSynID, a framework for generating high-resolution synthetic face datasets with user-defined identity attribute distributions and paired document-style and trusted live capture images. The dataset generated using FLUXSynID shows improved alignment with real-world identity distributions and greater diversity compared to prior work. I will also discuss how FLUXSynID’s dataset and generation tools can support research in face recognition and morphing attack detection (MAD), enhancing model robustness in both academic and practical applications.

An Ecological and Objective Neural Marker of Implicit Unfamiliar Identity Recognition

We developed a novel paradigm measuring implicit identity recognition using Fast Periodic Visual Stimulation (FPVS) with EEG among 16 students and 12 police officers with normal face processing abilities. Participants' neural responses to a 1-Hz tagged oddball identity embedded within a 6-Hz image stream revealed implicit recognition with high-quality mugshots but not CCTV-like images, suggesting optimal resolution requirements. Our findings extend previous research by demonstrating that even unfamiliar identities can elicit robust neural recognition signatures through brief, repeated passive exposure. This approach offers potential for objective validation of face processing abilities in forensic applications, including assessment of facial examiners, Super-Recognisers, and eyewitnesses, potentially overcoming limitations of traditional behavioral assessment methods.

Single-neuron correlates of perception and memory in the human medial temporal lobe

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

Using Fast Periodic Visual Stimulation to measure cognitive function in dementia

Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

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.

Oligodendrocyte dyfunction drives human cognitive decline

Altered grid-like coding in early blind people and the role of vision in conceptual navigation

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Digital Minds: Brain Development in the Age of Technology

Digital Minds: Brain Development in the Age of Technology examines how our increasingly connected world shapes mental and cognitive health. From screen time and social media to virtual interactions, this seminar delves into the latest research on how technology influences brain development, relationships, and emotional well-being. Join us to explore strategies for harnessing technology's benefits while mitigating its potential challenges, empowering you to thrive in a digital age.

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.

Enhancing Real-World Event Memory

Memory is essential for shaping how we interpret the world, plan for the future, and understand ourselves, yet effective cognitive interventions for real-world episodic memory loss remain scarce. This talk introduces HippoCamera, a smartphone-based intervention inspired by how the brain supports memory, designed to enhance real-world episodic recollection by replaying high-fidelity autobiographical cues. It will showcase how our approach improves memory, mood, and hippocampal activity while uncovering links between memory distinctiveness, well-being, and the perception of time.

Gene regulatory mechanisms of neocortex development and evolution

The neocortex is considered to be the seat of higher cognitive functions in humans. During its evolution, most notably in humans, the neocortex has undergone considerable expansion, which is reflected by an increase in the number of neurons. Neocortical neurons are generated during development by neural stem and progenitor cells. Epigenetic mechanisms play a pivotal role in orchestrating the behaviour of stem cells during development. We are interested in the mechanisms that regulate gene expression in neural stem cells, which have implications for our understanding of neocortex development and evolution, neural stem cell regulation and neurodevelopmental disorders.

Mind Perception and Behaviour: A Study of Quantitative and Qualitative Effects

Unmotivated bias

In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Targeting gamma oscillations to improve cognition

Principles of Cognitive Control over Task Focus and Task

2024 BACN Mid-Career Prize Lecture Adaptive behavior requires the ability to focus on a current task and protect it from distraction (cognitive stability), and to rapidly switch tasks when circumstances change (cognitive flexibility). How people control task focus and switch-readiness has therefore been the target of burgeoning research literatures. Here, I review and integrate these literatures to derive a cognitive architecture and functional rules underlying the regulation of stability and flexibility. I propose that task focus and switch-readiness are supported by independent mechanisms whose strategic regulation is nevertheless governed by shared principles: both stability and flexibility are matched to anticipated challenges via an incremental, online learner that nudges control up or down based on the recent history of task demands (a recency heuristic), as well as via episodic reinstatement when the current context matches a past experience (a recognition heuristic).

Sophie Scott - The Science of Laughter from Evolution to Neuroscience

Keynote Address to British Association of Cognitive Neuroscience, London, 10th September 2024

Prosocial Learning and Motivation across the Lifespan

2024 BACN Early-Career Prize Lecture Many of our decisions affect other people. Our choices can decelerate climate change, stop the spread of infectious diseases, and directly help or harm others. Prosocial behaviours – decisions that help others – could contribute to reducing the impact of these challenges, yet their computational and neural mechanisms remain poorly understood. I will present recent work that examines prosocial motivation, how willing we are to incur costs to help others, prosocial learning, how we learn from the outcomes of our choices when they affect other people, and prosocial preferences, our self-reports of helping others. Throughout the talk, I will outline the possible computational and neural bases of these behaviours, and how they may differ from young adulthood to old age.

Llama 3.1 Paper: The Llama Family of Models

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Gender, trait anxiety and attentional processing in healthy young adults: is a moderated moderation theory possible?

Three studies conducted in the context of PhD work (UNIL) aimed at proving evidence to address the question of potential gender differences in trait anxiety and executive control biases on behavioral efficacy. In scope were male and female non-clinical samples of adult young age that performed non-emotional tasks assessing basic attentional functioning (Attention Network Test – Interactions, ANT-I), sustained attention (Test of Variables of Attention, TOVA), and visual recognition abilities (Object in Location Recognition Task, OLRT). Results confirmed the intricate nature of the relationship between gender and health trait anxiety through the lens of their impact on processing efficacy in males and females. The possibility of a gendered theory in trait anxiety biases is discussed.

Navigating semantic spaces: recycling the brain GPS for higher-level cognition

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machinery that primarily evolved, in lower level animals, to represent relationships between spatial locations and navigate space, in humans are reused to encode relationships between concepts in an internal abstract representational space of meaning.

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.

The Role of Cognitive Appraisal in the Relationship between Personality and Emotional Reactivity

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.

Characterizing the causal role of large-scale network interactions in supporting complex cognition

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

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.

Stability of visual processing in passive and active vision

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.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Of glia and macrophages, signaling hubs in development and homeostasis

We are interested in the biology of macrophages, which represent the first line of defense against pathogens. In Drosophila, the embryonic hemocytes arise from the mesoderm whereas glial cells arise from multipotent precursors in the neurogenic region. These cell types represent, respectively, the macrophages located outside and within the nervous system (similar to vertebrate microglia). Thus, despite their different origin, hemocytes and glia display common functions. In addition, both cell types express the Glide/Gcm transcription factor, which plays an evolutionarily conserved role as an anti-inflammatory factor. Moreover, embryonic hemocytes play an evolutionarily conserved and fundamental role in development. The ability to migrate and to contact different tissues/organs most likely allow macrophages to function as signaling hubs. The function of macrophages beyond the recognition of the non-self calls for revisiting the biology of these heterogeneous and plastic cells in physiological and pathological conditions across evolution.

Where Cognitive Neuroscience Meets Industry: Navigating the Intersections of Academia and Industry

In this talk, Mirta will share her journey from her education a mathematically-focused high school to her currently unconventional career in London, emphasizing the evolution from a local education in Croatia to international experiences in the US and UK. We will explore the concept of interdisciplinary careers in the modern world, viewing them through the framework of increasing demand, flexibility, and dynamism in the current workplace. We will underscore the significance of interdisciplinary research for launching careers outside of academia, and bolstering those within. I will challenge the conventional norm of working either in academia or industry, and encourage discussion about the opportunities for combining the two in a myriad of career opportunities. I’ll use examples from my own and others’ research to highlight opportunities for early career researchers to extend their work into practical applications. Such an approach leverages the strengths of both sectors, fostering innovation and practical applications of research findings. I hope these insights can offer valuable perspectives for those looking to navigate the evolving demands of the global job market, illustrating the advantages of a versatile skill set that spans multiple disciplines and allows extensions into exciting career options.

Visual mechanisms for flexible behavior

Perhaps the most impressive aspect of the way the brain enables us to act on the sensory world is its flexibility. We can make a general inference about many sensory features (rating the ripeness of mangoes or avocados) and map a single stimulus onto many choices (slicing or blending mangoes). These can be thought of as flexibly mapping many (features) to one (inference) and one (feature) to many (choices) sensory inputs to actions. Both theoretical and experimental investigations of this sort of flexible sensorimotor mapping tend to treat sensory areas as relatively static. Models typically instantiate flexibility through changing interactions (or weights) between units that encode sensory features and those that plan actions. Experimental investigations often focus on association areas involved in decision-making that show pronounced modulations by cognitive processes. I will present evidence that the flexible formatting of visual information in visual cortex can support both generalized inference and choice mapping. Our results suggest that visual cortex mediates many forms of cognitive flexibility that have traditionally been ascribed to other areas or mechanisms. Further, we find that a primary difference between visual and putative decision areas is not what information they encode, but how that information is formatted in the responses of neural populations, which is related to difference in the impact of causally manipulating different areas on behavior. This scenario allows for flexibility in the mapping between stimuli and behavior while maintaining stability in the information encoded in each area and in the mappings between groups of neurons.

Memory: types and neuroanatomical basis

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).

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.

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.

Trends in NeuroAI - SwiFT: Swin 4D fMRI Transformer

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

A synergistic core for human brain evolution and cognition

Movements and engagement during decision-making

When experts are immersed in a task, a natural assumption is that their brains prioritize task-related activity. Accordingly, most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. Surprisingly, we observed that during decision-making, the cortex-wide activity of multiple cell types is dominated by movements, especially “uninstructed movements”, that are spontaneously expressed. These observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. To understand the relationship between these movements and decision-making, we examined the movements more closely. We tested whether the magnitude or the timing of the movements was correlated with decision-making performance. To do this, we partitioned movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This hinted that the timing of spontaneous movements could indicate periods of disengagement. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity. Surprisingly, we found that the same movement impacts neural activity more strongly when animals are disengaged. An intriguing possibility is that these larger movement signals disrupt cognitive computations, leading to poor decision-making performance. Taken together, these observations argue that movements and cognitionare closely intertwined, even during expert decision-making.

Identifying mechanisms of cognitive computations from spikes

Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting complex goal-directed behavior. These signals mix in heterogeneous responses of single neurons, making it difficult to untangle underlying mechanisms. I will present two approaches for revealing interpretable circuit mechanisms from heterogeneous neural responses during cognitive tasks. First, I will show a flexible nonparametric framework for simultaneously inferring population dynamics on single trials and tuning functions of individual neurons to the latent population state. When applied to recordings from the premotor cortex during decision-making, our approach revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Second, I will show an approach for inferring an interpretable network model of a cognitive task—the latent circuit—from neural response data. We developed a theory to causally validate latent circuit mechanisms via patterned perturbations of activity and connectivity in the high-dimensional network. This work opens new possibilities for deriving testable mechanistic hypotheses from complex neural response data.

Neuroinflammation in Epilepsy: what have we learned from human brain tissue specimens ?

Epileptogenesis is a gradual and dynamic process leading to difficult-to-treat seizures. Several cellular, molecular, and pathophysiologic mechanisms, including the activation of inflammatory processes.  The use of human brain tissue represents a crucial strategy to advance our understanding of the underlying neuropathology and the molecular and cellular basis of epilepsy and related cognitive and behavioral comorbidities,  The mounting evidence obtained during the past decade has emphasized the critical role of inflammation  in the pathophysiological processes implicated in a large spectrum of genetic and acquired forms of  focal epilepsies. Dissecting the cellular and molecular mediators of  the pathological immune responses and their convergent and divergent mechanisms, is a major requisite for delineating their role in the establishment of epileptogenic networks. The role of small regulatory molecules involved in the regulation of  specific pro- and anti-inflammatory pathways  and the crosstalk between neuroinflammation and oxidative stress will be addressed.    The observations supporting the activation of both innate and adaptive immune responses in human focal epilepsy will be discussed and elaborated, highlighting specific inflammatory pathways as potential targets for antiepileptic, disease-modifying therapeutic strategies.

Rhythms for cognition: Learning, routing and top-down modulation

Vocal emotion perception at millisecond speed

The human voice is possibly the most important sound category in the social landscape. Compared to other non-verbal emotion signals, the voice is particularly effective in communicating emotions: it can carry information over large distances and independent of sight. However, the study of vocal emotion expression and perception is surprisingly far less developed than the study of emotion in faces. Thereby, its neural and functional correlates remain elusive. As the voice represents a dynamically changing auditory stimulus, temporally sensitive techniques such as the EEG are particularly informative. In this talk, the dynamic neurocognitive operations that take place when we listen to vocal emotions will be specified, with a focus on the effects of stimulus type, task demands, and speaker and listener characteristics (e.g., age). These studies suggest that emotional voice perception is not only a matter of how one speaks but also of who speaks and who listens. Implications of these findings for the understanding of psychiatric disorders such as schizophrenia will be discussed.

Ecological Psychology Today

Brain Connectivity Workshop

Founded in 2002, the Brain Connectivity Workshop (BCW) is an annual international meeting for in-depth discussions of all aspects of brain connectivity research. By bringing together experts in computational neuroscience, neuroscience methodology and experimental neuroscience, it aims to improve the understanding of the relationship between anatomical connectivity, brain dynamics and cognitive function. These workshops have a unique format, featuring only short presentations followed by intense discussion. This year’s workshop is co-organised by Wellcome, putting the spotlight on brain connectivity in mental health disorders. We look forward to having you join us for this exciting, thought-provoking and inclusive event.

Distributed dynamics and cognition in the multiregional neocortex

Bernstein Conference 2024

Non-Human Recognition of Orthography: How is it implemented and how does it differ from Human orthographic processing

Bernstein Conference 2024

Action recognition best explains neural activity in cuneate nucleus

COSYNE 2022

Do better object recognition models improve the generalization gap in neural predictivity?

COSYNE 2022

Linking neural dynamics across macaque V4, IT, and PFC to trial-by-trial object recognition behavior

COSYNE 2022

Linking neural dynamics across macaque V4, IT, and PFC to trial-by-trial object recognition behavior

COSYNE 2022

Distinct roles of excitatory and inhibitory neurons in the macaque IT cortex in object recognition

COSYNE 2023

Leveraging computational and animal models of vision to probe atypical emotion recognition in autism

COSYNE 2023

On-line SEUDO for real-time cell recognition in Calcium Imaging

COSYNE 2023

Spatial-frequency channels for object recognition by neural networks are twice as wide as those of humans

COSYNE 2023

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

COSYNE 2023

Geometric Signatures of Speech Recognition: Insights from Deep Neural Networks to the Brain

COSYNE 2025

Age-related hearing loss in older adults and cognition in older adults: Preliminary findings

FENS Forum 2024

The analysis of the OXT-DA interaction causing social recognition deficit in Syntaxin1A KO

FENS Forum 2024

The association between reduced respiratory function and cognition in amyotrophic lateral sclerosis

FENS Forum 2024

Behavioral impacts of simulated microgravity on male mice: Locomotion, social interactions and memory in a novel object recognition task

FENS Forum 2024

The cortical amygdala mediates individual recognition in mice

FENS Forum 2024

Cross-cultural cognition: Working memory in South African young adults

FENS Forum 2024

A deep learning approach for the recognition of behaviors in the forced swim test

FENS Forum 2024

Dieckol as a novel neuroprotective candidate with cognition improvement and multifaceted mechanisms in Alzheimer's disease mouse model

FENS Forum 2024

Direct electrical stimulation of the human amygdala enhances recognition memory for objects but not scenes

FENS Forum 2024

Two distinct ways to form long-term object recognition memory during sleep and wakefulness

FENS Forum 2024

Early disruption in social recognition and its impact on episodic memory in triple transgenic mice model of Alzheimer’s disease

FENS Forum 2024

Effect of high-intensity interval and moderate-intensity continuous training on neuroplasticity, cognition, and sensorimotor performance in aged rats

FENS Forum 2024

Enacted cognition: Pigeons’ pecking behavior is predictive of their upcoming choice

FENS Forum 2024

Evaluation of novel object recognition test results of rats injected with intracerebroventricular streptozocin to develop Alzheimer's disease models

FENS Forum 2024

Exploring the impact of interthalamic adhesion on human cognition: Insights from healthy subjects and thalamic stroke patients

FENS Forum 2024

GnRH and miR-200b treatments boost cognition in Down syndrome

FENS Forum 2024

HBK-15 rescues recognition memory in MK-801- and stress-induced cognitive impairments in female mice

FENS Forum 2024

Hippocampal coding: A study on spatial cognition in pigeons

FENS Forum 2024

Homecage-based unsupervised novel object recognition in mice

FENS Forum 2024

Impact of stress on cognition and prefrontal networks

FENS Forum 2024

Interaction of sex and sleep on performance at the novel object recognition task in mice

FENS Forum 2024

Investigating the recruitment of parvalbumin and somatostatin interneurons into engrams for associative recognition memory

FENS Forum 2024

Investigating the role of Nup153 in neuronal responsiveness and its link to cognition

FENS Forum 2024

Mapping social cognition in patients with gliomas: Preoperative and intraoperative insights from fMRI, MEG, and direct electrical stimulation

FENS Forum 2024

Microbiome depletion alters spatial cognition and hippocampal place cell ensembles

FENS Forum 2024

Mouse can recognize other individuals: Maternal exposure to dioxin does not affect identification but perturbs the recognition ability of other individuals

FENS Forum 2024

How much data is enough to reliably measure individual differences in cognition?

FENS Forum 2024

Myoelectric gesture recognition in patients with spinal cord injury using a medium-density EMG system

FENS Forum 2024