The Insanally Lab is hiring postdocs to study the neural basis of auditory perception and learning. We incorporate a wide range of techniques including behavioral paradigms, in vivo multi-region neural recordings, optogenetics, chemogenetics, fiber photometry, and novel computational methods. Our lab is super supportive, collaborative, and we take mentoring seriously! Located at Pitt, our lab is part of a large systems neuroscience community that includes CNBC and CMU. For inquiries, feel free to reach out to me here: mni@pitt.edu. To find out more about our work, visit Insanallylab.com

The PhD in Medical Sciences: The University of Nicosia Medical School offers the degree PhD in Medical Sciences. The degree is awarded to students who successfully complete an independent research programme that breaks new ground in the chosen field of study. The PhD programme aspires to empower students to become independent researchers, thus advancing innovation and development. The Research Project: We are currently inviting application through a competitive process for high calibre candidates to apply for one PhD Scholarship in the field of Neuroscience. The successful candidate will enrol on the PhD programme in Medical Sciences and will work under the Supervision of Prof Avgis Hadjipapas, Professor for Neuroscience and Research Methods at the University of Nicosia Medical School. The project is based on an international collaboration between the University of Nicosia Medical School, (UN) the University Maastricht University Medical Center (MUMC), Maastricht University (MU) and McGill University (McGill U). The project predominantly involves data-analysis (signal processing), which means that a large part of the project can be conducted remotely. Project Description: Title of research project: Characterization of circadian rhythm modulations in intracranial EEG and their relationship to seizure onsets in focal epilepsy Background, rationale and objectives: Epilepsy affects roughly 1% of the population, and about a third of patients have unpredictable seizures which cannot be adequately controlled with medication (Kuhlmann et al., 2018). Therefore, better understanding of seizure generation and improving seizure predictability are central goals in epilepsy research to prevent seizures from occurring. Recent investigations by our own (Mitsis et al., 2020) and other groups (Leguia et al., 2021) have shown that seizure onsets exhibit a tight correlation to certain phases of circadian rhythms, which leads to improved seizure predictability. However, our previous work (Mitsis et al., 2020) only utilized surface EEG. In this project, and based on a collaboration formed between the University of Nicosia Medical School (UN), Maastricht University Medical Center (MUMC), Maastricht University (MU), and McGill University (McGill U), we will address this question by examining intracranial recordings provided by the MUMC partner, obtained directly from the area of the suspected epileptogenic focus. We will first characterize in detail the circadian variation of signal parameters extracted from the intracranial EEG. We will then examine whether seizure onsets are phase coupled (correlated) to these circadian modulations. This will inform both important pathophysiological questions in terms of the extent of the functional seizure generating network. Further, analysis of this correlation at the level of individual patient recordings will inform the feasibility of seizure forecasting informed by circadian rhythms. Successful candidates will benefit from interacting with an international and interdisciplinary consortium of neuroscientists, neurologists and engineers throughout the duration of the project. References Karoly, P.J., Ung, H., Grayden, D.B., Kuhlmann, L., Leyde, K., Cook, M.J., Freestone, D.R., 2017. The circadian profile of epilepsy improves seizure forecasting. Brain 140, 2169–2182. https://doi.org/10.1093/brain/awx173 Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P., 2018. Seizure prediction — ready for a new era. Nat. Rev. Neurol. https://doi.org/10.1038/s41582-018-0055-2 Leguia, M.G., Andrzejak, R.G., Rummel, C., Fan, J.M., Mirro, E.A., Tcheng, T.K., Rao, V.R., Baud, M.O., 2021. Seizure Cycles in Focal Epilepsy. JAMA Neurol. In press, 1–10. https://doi.org/10.1001/jamaneurol.2020.5370 Mitsis, G.D., Anastasiadou, M.N., Christodoulakis, M., Papathanasiou, E.S., Papacostas, S.S., Hadjipapas, A., 2020. Functional brain networks of patients with epilepsy exhibit pronounced multiscale periodicities, which correlate with seizure onset. Hum. Brain Mapp. hbm.24930. https://doi.org/10.1002/hbm.24930 The Scholarship: The Scholarship will have a duration of three to four years and will cover: • The tuition fees for the PhD programme which are €13,500 in total for the first 3 years and €1,500 for year 4. • A monthly stipend of €1,000 for the duration of three to four years. Application for the PhD Scholarship: Candidates should submit an online application through this link and upload the following supporting documents: • A cover letter clearly stating that they apply for the PhD Scholarship in the field of Neuroscience for the PhD Research Project ‘Characterization of circadian rhythm modulations in intracranial EEG and their relationship to seizure onsets in focal epilepsy.’ • Copies of the applicant’s qualifications/degree(s) – the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Copies of the applicant’s transcript(s) - the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Proof of English language proficiency such as IELTS with a score of 7 overall and with a minimum score of 7 in writing or TOEFL iBT with a score of 94 overall and a minimum score of 27 in Writing. Other internationally recognized English language qualifications might be considered upon review. Students from the UK, Ireland USA, Canada (from English speaking provinces), Australia and New Zealand are exempt from the English language requirement. • Two reference letters, of which at least one should be from an academic. • A full Curriculum Vitae (CV). Applications should be submitted by Friday, July 29, 2022 at 5pm. Only fully completed applications, containing all necessary supporting documents will be reviewed. Only candidates who are shortlisted will be contacted and invited to an interview.

The Georgia Institute of Technology is one of the top ranked institutions in the country and ranks as one of the best places to work. The School of Psychology and Undergraduate Program in Neuroscience in the College of Sciences invites applications for a full-time, non-tenure-track Academic Professional faculty position, which is a Teaching Faculty and Academic Advisor position, beginning July 1st 2022 (earlier start possible). The successful candidate will join a vibrant group of faculty with interests in brain, cognition, behavior and (neuro)technology as well as innovative pedagogy and research in those fields. The Academic Professional faculty member will be primarily responsible for teaching courses in the undergraduate neuroscience curriculum. Additional duties include academic advising, course development, and program assessment. The position provides opportunities for program and professional development, as well as for promotion through the non-tenured faculty track. Preference will be given to applicants who are well prepared to teach neuroscience and who have strong background in quantitative and computational methods. The applicant must have a PhD in neuroscience, psychology or a related discipline and experience with teaching undergraduate neuroscience and/or psychology-related coursework. Applicants should provide a letter of intent, curriculum vita, teaching statement, and the names and contact information for two references. Applications can be submitted electronically in PDF format to (applicant portal). Review of applications will begin immediately and will continue until the position is filled. Georgia Tech is a top-ranked public research university situated in the heart of Atlanta, a diverse and vibrant city with great economic and cultural strengths. The Institute is a member of the University System of Georgia, the Georgia Research Alliance, and the Association of American Universities. Georgia Tech prides itself on its technology resources, collaborations, high-quality student body, and its commitment to diversity, equity, and inclusion. Georgia Tech is an equal education/employment opportunity institution dedicated to building a diverse community. We strongly encourage applications from women, underrepresented minorities, individuals with disabilities, and veterans. Georgia Tech has policies to promote a healthy work-life balance and is aware that attracting faculty may require meeting the needs of two careers.

The Georgia Institute of Technology is one of the top ranked institutions in the country and ranks as one of the best places to work. The School of Psychology and Undergraduate Program in Neuroscience in the College of Sciences invites applications for a full-time, non-tenure-track Academic Professional faculty position, which is a Teaching Faculty and Academic Advisor position, beginning July 1st 2022 (earlier start possible). The successful candidate will join a vibrant group of faculty with interests in brain, cognition, behavior and (neuro)technology as well as innovative pedagogy and research in those fields. The Academic Professional faculty member will be primarily responsible for teaching courses in the undergraduate neuroscience curriculum. Additional duties include academic advising, course development, and program assessment. The position provides opportunities for program and professional development, as well as for promotion through the non-tenured faculty track. Preference will be given to applicants who are well prepared to teach neuroscience and who have strong background in quantitative and computational methods. The applicant must have a PhD in neuroscience, psychology or a related discipline and experience with teaching undergraduate neuroscience and/or psychology-related coursework. Applicants should provide a letter of intent, curriculum vita, teaching statement, and the names and contact information for two references. Applications can be submitted electronically in PDF format to (applicant portal). Review of applications will begin immediately and will continue until the position is filled. Georgia Tech is a top-ranked public research university situated in the heart of Atlanta, a diverse and vibrant city with great economic and cultural strengths. The Institute is a member of the University System of Georgia, the Georgia Research Alliance, and the Association of American Universities. Georgia Tech prides itself on its technology resources, collaborations, high-quality student body, and its commitment to diversity, equity, and inclusion. Georgia Tech is an equal education/employment opportunity institution dedicated to building a diverse community. We strongly encourage applications from women, underrepresented minorities, individuals with disabilities, and veterans. Georgia Tech has policies to promote a healthy work-life balance and is aware that attracting faculty may require meeting the needs of two careers.

The Center for Neural Science at New York University (NYU), jointly with the Center for Computational Neuroscience (CCN) at the Flatiron Institute of the Simons Foundation, invites applications for an open rank joint position, with a preference for junior or mid-career candidates. We seek exceptional candidates that use computational frameworks to develop concepts, models, and tools for understanding brain function. Areas of interest include sensory representation and perception, memory, decision-making, adaptation and learning, and motor control. A Ph.D. in a relevant field, such as neuroscience, engineering, physics or applied mathematics, is required. Review of applications will begin 28 March 2021. Further information: * Joint position: https://apply.interfolio.com/83845 * NYU Center for Neural Science: https://www.cns.nyu.edu/ * Flatiron Institute Center for Computational Neuroscience: https://www.simonsfoundation.org/flatiron/center-for-computational-neuroscience/

Do you want to illuminate the “dark matter of the brain” by watching neuromodulators and their intracellular effectors in action? Do you wonder why we spend a third of our life sleeping? Do you seek to become a bridge builder between cellular and systems neuroscience? Two postdoctoral positions are available to investigate the role of neuromodulator actions and sleep functions in Dr. Yao Chen’s laboratory in the Department of Neuroscience at Washington University in St. Louis. The first project will investigate how neuromodulators are interpreted via the spatial and temporal features of intracellular signals to play critical roles in cellular physiology and behavior. The second project investigates the mechanisms by which sleep supports cellular and organismal functions. We accomplish both goals by measuring and perturbing the dynamics of biological signals inside and outside the cell. We develop and employ a variety of techniques ex vivo and in vivo, including two-photon fluorescence lifetime imaging microscopy, electrophysiology, biosensor design, opto/chemogenetics, molecular biology, pharmacology, and behavior analyses. For additional information see: https://sites.wustl.edu/yaochenlab/. The PI is committed to mentoring and to nurturing a creative, thoughtful, and collaborative lab culture. Washington University neuroscience community is scientifically excellent and exceptionally collegial. The School of Medicine is consistently ranked among the top 5 medical schools in the United States, with extensive infrastructural and core facility support, and a dynamic research environment in many areas of basic and clinical science. Postdocs are also supported through a dedicated Office of Postdoctoral Affairs and an active Postdoc Society with many professional development opportunities. The St. Louis area combines the attractions of a major city with affordable lifestyle opportunities. The position comes with a competitive salary and a generous benefit package. We are looking for highly motivated individuals who are independent and committed to scientific discovery. The candidates should have expertise in optical imaging and are skilled in quantitative data analyses. Experience in neuromodulator signaling, circadian rhythm or sleep biology, and expertise in electrophysiology, animal behavior, or systems neuroscience are valued. Our work is interdisciplinary and will benefit from diverse perspectives, including molecular and cell biology, systems biology, biophysics, pharmacology, and engineering – even if your past work is not directly related to neuromodulators or sleep, you might be a great fit for the position. Interested candidates should send the following to yaochen@wustl.edu. 1) a cover letter explaining motivation, research experience, and interests; 2) CV; 3) the names of three references.

Join our unique transatlantic PhD program in neuroscience! The International Max Planck Research School (IMPRS) for Brain and Behavior is a unique transatlantic collaboration between two Max Planck Neuroscience institutes – the Max Planck-associated research center caesar and the Max Planck Florida Institute for Neuroscience – and the partner universities, University of Bonn and Florida Atlantic University. It offers a completely funded international PhD program in neuroscience in either Bonn, Germany, or Jupiter, Florida. We offer an exciting opportunity to outstanding Bachelor's and/or Master's degree holders (or equivalent) from any field (life sciences, mathematics, physics, computer science, engineering, etc.) to be immersed in a stimulating environment that provides novel technologies to elucidate the function of brain circuits from molecules to animal behavior. The comprehensive and diverse expertise of the faculty in the exploration of brain-circuit function using advanced imaging and optogenetic techniques combined with comprehensive training in fundamental neurobiology will provide students with an exceptional level of knowledge to pursue a successful independent research career. Apply to Bonn, Germany by November 15, 2020 or to Florida, USA by December 1, 2020!

We invite applications for a Research Software Engineer (RSE) position with expertise in software development, machine learning, neural data analysis, and experimental control (ideally with the Bonsai ecosystem) to contribute to the recently funded project 'Machine Learning for Neuroscience Experimental Control'. You will contribute to the neuroscience community with advanced machine learning software for experimental control. You will be embedded in the unparalleled research environment of the Gatsby Unit, the SWC and NeuroGEARS, with opportunities to connect with top researchers and engineers.

The project aims at answering an almost 100 year old question in Neuroscience: “What are spikes good for?”. Indeed, since the discovery of action potentials by Lord Adrian in 1926, it has remained largely unknown what the benefits of spiking neurons are, when compared to analog neurons. Traditionally, it has been argued that spikes are good for long-distance communication or for temporally precise computation. However, there is no systematic study that quantitatively compares the communication as well as the computational benefits of spiking neuron w.r.t analog neurons. The aim of the project is to systematically quantify the benefits of spiking at various levels. The PhD students and post-doc will be supervised by Prof. Jean-Pascal Pfister (Theoretical Neuroscience Group, Department of Physiology, University of Bern).

The Department of Biology at Washington University in St. Louis seeks a neuroscientist for a tenure-track position at the Assistant or Associate Professor level. The successful candidate will establish a research program focused on cutting-edge questions in developmental, cellular or systems neuroscience with particular interest in neuroethology, biologically-inspired artificial intelligence, evolution, or neural computation. The successful candidate will: join a vibrant neuroscience community; contribute to advising, mentoring, and teaching; and develop an externally funded and internationally recognized research program.

The lab of Rune W. Berg is looking for a highly motivated and dynamic researcher for a 3-year position to start January 1st, 2024. The topic is the neuroscience of motor control with a focus on locomotion and spinal circuitry and connections with the brain. The person will be performing the following: 1) experimental recording of neurons in the brain and spinal cord of awake behaving rats using Neuropixels and Neuronexus electrodes combined with optogenetics. 2) Analyze the large amount of data generated from these experiments, including tissue processing. 3) Participate in the development of the new theory of motor control.

Bilkent University invites applications for multiple open-rank faculty positions in the Department of Neuroscience. The department plans to expand research activities in certain focus areas and accordingly seeks applications from promising or established scholars who have worked in the following or related fields: Cellular/molecular/developmental neuroscience with a strong emphasis on research involving animal models. Systems/cognitive/computational neuroscience with a strong emphasis on research involving emerging data-driven approaches, including artificial intelligence, robotics, brain-machine interfaces, virtual reality, computational imaging, and theoretical modeling. Candidates with a research focus in those areas whose research has a neuroimaging component are particularly encouraged to apply. The Department’s interdisciplinary Graduate Program in Neuroscience that offers Master's and PhD degrees was established in 2014. The department is affiliated with Bilkent’s Aysel Sabuncu Brain Research Center (ASBAM) and the National Magnetic Resonance Research Center (UMRAM). Faculty affiliated with the department has the privilege to access state-of-the-art research facilities in these centers, including animal facilities, cellular/molecular laboratory infrastructure, psychophysics laboratories, eyetracking laboratories, EEG laboratories, a human-robot interaction laboratory, and two MRI scanners (3T and 1.5T).

The postdoc position is focused on the development of BrainSTEM, a web application designed as an electronic lab notebook for describing neurophysiological experiments as well as a data-sharing platform for the community. The role involves the design of a standard language for describing experimental neuroscience, semantic search functionality, stronger adoption of the FAIR principles, and stimulating and supporting community uptake. The project is primarily funded by the NIH, through the Brain Initiative U19 Oxytocin grant. The project will include occasional travels, e.g., to New York (NYU), Brain Initiate meetings, SfN, FENS, and to pilot user labs.

The lab of Rune W. Berg is looking for a highly motivated and dynamic researcher for a 3-year position to start January 1st, 2024. The topic is the neuroscience of motor control with a focus on locomotion and spinal circuitry and connections with the brain. The person will be performing the following: 1) experimental recording of neurons in the brain and spinal cord of awake behaving rats using Neuropixels and Neuronexus electrodes combined with optogenetics. 2) Analyze the large amount of data generated from these experiments, including tissue processing. 3) Participate in the development of the new theory of motor control.

The International Graduate School of Neuroscience at Ruhr University Bochum offers two four year PhD Scholarships financed through the DAAD for experimentalists as well as computational people.

The International M.Sc. Program at The Sagol School of Neuroscience, 2024-25, at Tel Aviv University is a two-year program designed to provide interdisciplinary thinking and knowledge to join the next generation of world-leading neuroscientists. The program is held at the renowned Sagol School of Neuroscience and will train students in the latest cutting-edge neuroscience fields related to biology, psychology, engineering, and other related fields.

The Neuroscience Institute at Jersey Shore University Medical Center, New Jersey, USA is seeking a postdoctoral fellow to work on basic, clinical, and translational projects in the fields of seizures, epilepsy, human intracranial EEG, signal processing, cognition and consciousness. The fellow will join a multidisciplinary team of five epileptologists, neurosurgeons, epilepsy nurses, nurse practitioners, neuropsychologists and researchers providing holistic care to patients with epilepsy. The postdoctoral fellows will have access to the large clinical, imaging, and EEG databases, and outcome measures of cutting edge treatment modalities within the system for research purposes. The successful candidate will be well versed in data collection, processing, programming and will lead an independent research project working closely with collaborators and publish high-quality research.

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

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 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.

n the neurosciences the need for some 'overarching' theory is sometimes expressed, but it is not always obvious what is meant by this. One can perhaps agree that in modern science observation and experimentation is normally complemented by 'theory', i.e. the development of theoretical concepts that help guiding and evaluating experiments and measurements. A deeper discussion of 'brain theory' will require the clarification of some further distictions, in particular: theory vs. model and brain research (and its theory) vs. neuroscience. Other questions are: Does a theory require mathematics? Or even differential equations? Today it is often taken for granted that the whole universe including everything in it, for example humans, animals, and plants, can be adequately treated by physics and therefore theoretical physics is the overarching theory. Even if this is the case, it has turned out that in some particular parts of physics (the historical example is thermodynamics) it may be useful to simplify the theory by introducing additional theoretical concepts that can in principle be 'reduced' to more complex descriptions on the 'microscopic' level of basic physical particals and forces. In this sense, brain theory may be regarded as part of theoretical neuroscience, which is inside biophysics and therefore inside physics, or theoretical physics. Still, in neuroscience and brain research, additional concepts are typically used to describe results and help guiding experimentation that are 'outside' physics, beginning with neurons and synapses, names of brain parts and areas, up to concepts like 'learning', 'motivation', 'attention'. Certainly, we do not yet have one theory that includes all these concepts. So 'brain theory' is still in a 'pre-newtonian' state. However, it may still be useful to understand in general the relations between a larger theory and its 'parts', or between microscopic and macroscopic theories, or between theories at different 'levels' of description. This is what I plan to do.

Astrocytes release glutamate by regulated exocytosis in health and disease Vladimir Parpura, International Translational Neuroscience Research Institute, Zhejiang Chinese Medical University, Hangzhou, P.R. China Parpura will present you with the evidence that astrocytes, a subtype of glial cells in the brain, can exocytotically release the neurotransmitter glutamate and how this release is regulated. Spatiotemporal characteristic of vesicular fusion that underlie glutamate release in astrocytes will be discussed. He will also present data on a translational project in which this release pathway can be targeted for the treatment of glioblastoma, the deadliest brain cancer.

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.

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..

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.

The COSYNE 2025 conference was held in Montreal with post-conference workshops in Mont-Tremblant, continuing to provide a premier forum for computational and systems neuroscience. Attendees exchanged cutting-edge research in a single-track main meeting and in-depth specialized workshops, reflecting Cosyne’s mission to understand how neural systems function:contentReference[oaicite:6]{index=6}:contentReference[oaicite:7]{index=7}.

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.

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.

Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange:contentReference[oaicite:8]{index=8}. Bernstein Conference 2024, held in Frankfurt am Main, featured discussions, keynote lectures, and poster sessions, and has established itself as one of the most renowned conferences worldwide in this field:contentReference[oaicite:9]{index=9}:contentReference[oaicite:10]{index=10}.

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

Organised by FENS in partnership with the Austrian Neuroscience Association and the Hungarian Neuroscience Society, the FENS Forum 2024 will take place on 25–29 June 2024 in Vienna, Austria:contentReference[oaicite:0]{index=0}. The FENS Forum is Europe’s largest neuroscience congress, covering all areas of neuroscience from basic to translational research:contentReference[oaicite:1]{index=1}.

Advancements in cognitive neuroscience have provided profound insights into the workings of the human brain and the methods used offer opportunities to enhance performance, cognition, and mental health. Drawing upon interdisciplinary collaborations in the University of California San Diego, Human Performance Optimization Lab, this talk explores the application of cognitive neuroscience principles in three domains to improve human performance and alleviate mental health challenges. The first section will discuss studies addressing the role of vision and oculomotor function in athletic performance and the potential to train these foundational abilities to improve performance and sports outcomes. The second domain considers the use of electrophysiological measurements of the brain and heart to detect, and possibly predict, errors in manual performance, as shown in a series of studies with surgeons as they perform robot-assisted surgery. Lastly, findings from clinical trials testing personalized interventional treatments for mood disorders will be discussed in which the temporal and spatial parameters of transcranial magnetic stimulation (TMS) are individualized to test if personalization improves treatment response and can be used as predictive biomarkers to guide treatment selection. Together, these translational studies use the measurement tools and constructs of cognitive neuroscience to improve human performance and well-being.

Recent insights from auditory neuroscience provide a new perspective on how the brain encodes speech. Using these recent insights, I will provide an overview of key factors underpinning individual differences in children’s development of language and phonology, providing a context for exploring atypical reading development (dyslexia). Children with dyslexia are relatively insensitive to acoustic cues related to speech rhythm patterns. This lack of rhythmic sensitivity is related to the atypical neural encoding of rhythm patterns in speech by the brain. I will describe our recent data from infants as well as children, demonstrating developmental continuity in the key neural variables.

The COSYNE 2023 conference provided an inclusive forum for exchanging experimental and theoretical approaches to problems in systems neuroscience, continuing the tradition of bringing together the computational neuroscience community:contentReference[oaicite:5]{index=5}. The main meeting was held in Montreal followed by post-conference workshops in Mont-Tremblant, fostering intensive discussions and collaboration.

Neuromatch 5 (Neuromatch Conference 2022) was a fully virtual conference focused on computational neuroscience broadly construed, including machine learning work with explicit biological links:contentReference[oaicite:11]{index=11}. After four successful Neuromatch conferences, the fifth edition consolidated proven innovations from past events, featuring a series of talks hosted on Crowdcast and flash talk sessions (pre-recorded videos) with dedicated discussion times on Reddit:contentReference[oaicite:12]{index=12}.

The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function:contentReference[oaicite:2]{index=2}. The main meeting is single-track, with invited talks selected by the Executive Committee and additional talks and posters selected by the Program Committee based on submitted abstracts:contentReference[oaicite:3]{index=3}. The workshops feature in-depth discussion of current topics of interest in a small group setting:contentReference[oaicite:4]{index=4}.