The Neuroscience Department of the International School for Advanced Studies (SISSA; https://www.sissa.it/research/neuroscience) invites expressions of interest from scientists from various fields of Neuroscience for multiple tenure-track positions with anticipated start in 2025. Ongoing neuroscience research at SISSA includes cognitive neuroscience, computational and theoretical neuroscience, systems neuroscience, molecular and cellular research as well as genomics and genetics. The Department intends to potentiate its activities in these fields and to strengthen cross-field interactions. Expressions of interest from scientists in any of these fields are welcome. The working and teaching language of SISSA is English. This is an equal opportunity career initiative and we encourage applications from qualified women, racial and ethnic minorities, and persons with disabilities. Candidates should have a PhD in a relevant field and a proven record of research achievements. A clear potential to promote and lead research activities, and a specific interest in training and supervising PhD students is essential. Interested colleagues should present an original and innovative plan for their independent future research. We encourage both proposals within existing fields at SISSA as well as novel ideas outside of those or spanning various topics and methodologies of Neuroscience. SISSA is an international school promoting basic and applied research in Neuroscience, Mathematics and Physics and dedicated to the training of PhD students. Lab space and other resources will be commensurate with the appointment. Shared facilities include cell culture rooms, viral vector facilities, confocal microscopes, animal facilities, molecular and biochemical facilities, human cognition labs with EEG, TMS, and eye tracking systems, mechatronics workshop, and computing facilities. Agreements with national and international MRI scanning facilities are also in place. SISSA encourages fruitful exchanges between neuroscientists and other researchers including data scientists, physicists and mathematicians. Interested colleagues are invited to send a single pdf file including a full CV, a brief description of past and future research interests (up to 1,000 words), and the names of three referees to neuro.search@sissa.it. Selected candidates will be invited for an online or in-person seminar and 1- on-1 meetings in summer/autumn 2024. Deadline: A first evaluation round will consider all applications submitted before 15 May 2024. Later applications might be considered if no suitable candidates have been identified yet.

The project addresses the generation and functions of theta oscillations in spatial navigation using systems neuroscience and population-level approaches. The project involves performing electrophysiological recordings from freely moving animals using chronically implanted high-density Neuropixels silicon probes and applying optogenetics for single-cell tagging, and behavioral manipulations.

We are currently recruiting both a research technician and a fully funded PhD student to work on a Wellcome funded project 'How does the brain map sounds into the world?'. This Wellcome funded project uses a range of systems neuroscience and computational approaches to understand how auditory space is constructed in freely moving animals that are pursuing audio and audiovisual targets. The PhD student will be paid as a research assistant for four years, and have their fees funded at the UK rate.

The DEB Lab is seeking to hire highly motivated postdocs or research associates with an interest in systems neuroscience and experience in in vivo electrophysiology. The DEB Lab combines cutting-edge experimental approaches in non-human primates, including simultaneous neuroimaging, neuro-electrophysiology, and body physiology. The aim of the lab is to examine the structural pathways and functional mechanisms underlying the role of interoception in neural network dynamics as well as in behavioral and physiological correlates of subjective perceptual awareness.

The Neuroscience Department of the International School for Advanced Studies (SISSA) invites expressions of interest from scientists for multiple tenure-track positions in various fields of Neuroscience with anticipated start in 2025. The Department aims to enhance its activities in cognitive neuroscience, computational and theoretical neuroscience, systems neuroscience, molecular and cellular research, genomics, and genetics, and to strengthen cross-field interactions. The working and teaching language at SISSA is English. This is an equal opportunity career initiative.

We are looking for a postdoctoral researcher to develop new machine learning approaches for the analysis of large-scale extracellular recordings. The position is part of a wider effort to enable new discoveries with state-of-the-art electrode arrays and recording devices, and jointly supervised by Matthias Hennig and Matt Nolan. It offers a great opportunity to work with theoretical and experimental neuroscientists innovating open source tools and software for systems neuroscience.

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}.

It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are central to many explanations in systems neuroscience. Factors provide a framework for describing computational mechanism, and offer a bridge between data and concrete models. Yet there remains some discomfort with this abstraction, and with any attempt to provide mechanistic explanations above that of spikes, neurons, cell-types, and other comfortingly concrete entities. I will explain why, for many networks of spiking neurons, factors are not only a well-defined abstraction, but are critical to understanding computation mechanistically. Indeed, factors are as real as other abstractions we now accept: pressure, temperature, conductance, and even the action potential itself. I use recent empirical results to illustrate how factor-based hypotheses have become essential to the forming and testing of scientific hypotheses. I will also show how embracing factor-level descriptions affords remarkable power when decoding neural activity for neural engineering purposes.

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.

An influential theory in systems neuroscience suggests that brain function can be understood through low-dimensional dynamics [Vyas et al 2020]. However, a challenge in this framework is that a single computational task may involve a range of dynamic processes. To understand which processes are at play in the brain, it is important to use data on neural activity to constrain models. In this study, we present a method for extracting low-dimensional dynamics from data using low-rank recurrent neural networks (lrRNNs), a highly expressive and understandable type of model [Mastrogiuseppe & Ostojic 2018, Dubreuil, Valente et al. 2022]. We first test our approach using synthetic data created from full-rank RNNs that have been trained on various brain tasks. We find that lrRNNs fitted to neural activity allow us to identify the collective computational processes and make new predictions for inactivations in the original RNNs. We then apply our method to data recorded from the prefrontal cortex of primates during a context-dependent decision-making task. Our approach enables us to assign computational roles to the different latent variables and provides a mechanistic model of the recorded dynamics, which can be used to perform in silico experiments like inactivations and provide testable predictions.

The ability to record large neural populations—hundreds to thousands of cells simultaneously—is a defining feature of modern systems neuroscience. Aside from improved experimental efficiency, what do these technologies fundamentally buy us? I'll argue that they provide an exciting opportunity to move beyond studying the "average" neural response. That is, by providing dense neural circuit measurements in individual subjects and moments in time, these recordings enable us to track changes across repeated behavioral trials and across experimental subjects. These two forms of variability are still poorly understood, despite their obvious importance to understanding the fidelity and flexibility of neural computations. Scientific progress on these points has been impeded by the fact that individual neurons are very noisy and unreliable. My group is investigating a number of customized statistical models to overcome this challenge. I will mention several of these models but focus particularly on a new framework for quantifying across-subject similarity in stochastic trial-by-trial neural responses. By applying this method to noisy representations in deep artificial networks and in mouse visual cortex, we reveal that the geometry of neural noise correlations is a meaningful feature of variation, which is neglected by current methods (e.g. representational similarity analysis).

This session is a double feature of the Cologne Theoretical Neuroscience Forum and the Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience of the Jülich Research Center.

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

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}.

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

A major challenge of cognitive neuroscience is to understand how the brain as a network gives rise to our cognition. Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the opportunity to investigate brain connectivity not only via spatially distant, synchronous cerebrovascular hemodynamic responses (functional connectivity), but also glucose metabolism (metabolic connectivity). However, how these two modalities of brain connectivity differ in their relation to cognition is unknown. In this webinar, Dr Katharina Voigt will discuss recent findings demonstrating the advantage of simultaneous FDG-PET/fMRI in providing a more complete picture of the neural mechanisms underlying cognition, that calls for a combination of both modalities in future cognitive neuroscience. Dr Katharina Voigt is a Research Fellow within the Turner Institute for Brain and Mental Health, Monash University. Her research interests include systems neuroscience, simultaneous PET-MRI, and decision-making.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

The human brain network architecture can reveal crucial aspects of brain function and dysfunction. The topology of this network (known as the connectome) is shaped by a trade-off between wiring cost and network efficiency, and it has highly connected hub regions playing a prominent role in many brain disorders. By studying a landscape of plausible brain networks that preserve the wiring cost, fragile and resilient hubs can be identified. In this webinar, Dr Leonardo Gollo and Dr James Pang from Monash University will discuss this approach across the lifespan and some of its implications for neurodevelopmental and neurodegenerative diseases. Dr Leonardo Gollo is a Senior Research Fellow at the Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University. He holds an ARC Future Fellowship and his research interests include brain modelling, systems neuroscience, and connectomics. Dr James Pang is a Research Fellow at the Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University. His research interests are on combining neuroimaging and biophysical modelling to better understand the mechanisms of brain function in health and disease.

Using Systems Neuroscience Approaches to Understand Motor Learning & Recovery Post-Stroke

Jonathan Whitlock is an associate professor of neuroscience at the Kavli Institute for Systems Neuroscience in Trondheim, Norway. His group combines high-density single-unit recordings with silicone probes and sub-millimeter 3D tracking to study the cortical representation of pose and movement in freely behaving rats. The lecture will introduce his group’s work on neural tuning to pose and movement parietal and motor areas, and will include more recent findings from primary visual, auditory and somatosensory areas

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

The Aging Brain Initiative is an ambitious interdisciplinary effort by MIT focusing on understanding neurodegeneration and efforts to find hallmarks of aging, both in health and disease. The Initiative is broad, made up of scientists in several areas, including systems neuroscience, cell biology, engineering and computational biology, with core investigators from the Departments of Biology, Brain & Cognitive Sciences, Biological Engineering, and Computer Science & Artificial Intelligence Labs. "The theme of this symposium is Cellular & Molecular Mechanisms of Neurodegeneration.