Topic: systems neuroscience

Fully funded four-year PhD programme in systems neuroscience at the Sainsbury Wellcome Centre in central London. The first year centres on courses (Boot Camp, Systems and Theoretical Neuroscience, Experimental Neuroscience) and lab rotations, with intensive training in imaging, physiology, molecular and behavioural methods shared with Gatsby Computational Neuroscience Unit students; students design their project and begin research at the start of year two with SWC or affiliated faculty. Studentships cover UCL tuition fees for home and international students and include an annual tax-free stipe

NeuralNet is the annual scientific meeting of the GDR NeuralNet, a research network supported by the Centre National de la Recherche Scientifique (CNRS) that brings together researchers, engineers and specialists in neural data acquisition, analysis, interpretation and modelling, and fosters collaboration across systems neuroscience, computational modelling, NeuroAI and data science. The 15th annual meeting takes place from 25 to 27 November 2026 at Université Paris Cité, Campus Odéon, Paris. Keynote speakers are Asli Ayaz (VIB-KU Leuven Center for Neuroscience), Sophie Bagur (ESPCI), Joao Bar

Develop a human cognitive neuroscience programme using quantitative and computational approaches, supported by knowledge of neuroanatomy and systems neuroscience. The appointment sits in the Brain, Behavior and Cognition programme and is expected to use the Cognitive Neuroimaging Center. The role includes research, teaching, mentoring and collaboration across brain and cognitive health. Priority review is 15 November 2026.

COSYNE 2027 meets in Montréal, 11–16 March: main meeting 11–14 March and workshops 15–16 March. Registration opens 2 November 2026. COSYNE brings experimental and theoretical approaches together to understand how neural systems support behaviour and cognition. Research connects systems neuroscience with computational models, emphasizing explanations of brain function and exchange between empirical and theoretical researchers. The main meeting is at Fairmont The Queen Elizabeth and workshops are at Le Westin Montréal. Abstract submission is closed. Abstracts use double-blind review, with deci

The fifteenth Barcelona Computational, Cognitive and Systems Neuroscience meeting connects theoretical and experimental researchers, with particular emphasis on collaboration among Barcelona groups. Each day features eight to ten short research presentations and a poster session, alongside keynote lectures by Srdjan Ostojic and Nathalie Rochefort. Participants can contribute talks, posters or poster blitzes, with prizes for student contributions. The organizing committee comprises Sara Mederos, Chris Summerfield and Klaus Wimmer. Programme times, fees and submission and registration deadlines

The Sarvestani Lab at Cornell University is recruiting a postdoctoral scientist in systems neuroscience to study how visual and motor systems across the brain and body support perception and embodied cognition. Projects use cross-species behavioral experiments, multiphoton microscopy, electrophysiology, motion capture, and wireless sensors in tree shrews and rats.

The Stagkourakis Lab at Karolinska Institutet is recruiting a postdoctoral scholar to study neural circuits governing survival and homeostatic behaviors. The project combines systems neuroscience, Neuropixels recordings, imaging, circuit manipulation and transcriptomics in the SciLifeLab and Department of Neuroscience research environment.

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.

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 ce

Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision making. Evidence-based decision-making involves multiple brain regions across cortex, suggesting a distributed neural mechanism, but a precise description of interactions between regions remains an open problem. Addressing this problem requires developing new analysis methods that account for communication between areas, and pairing these with novel experimental approaches to capture neu

A fundamental problem in systems neuroscience is understanding how the brain learns the non-linear dynamics of the complex world and identifies the environment's state. Data-driven learning in such high-dimensional spaces requires lifting the curse of dimensionality. One way of reducing dimensionality relies on extracting from observed trajectories the underlying topological skeleton, i.e., fixed points connected by invariant manifolds. Thus, online extraction of fixed points from input trajectories is an important task. Whereas Dynamic Mode Decomposition (DMD) provides a framework to calculat

Systems neuroscience experiments generate multimodal data streams, including neuronal activity, stimuli, movement, and behavior events. In analysis, it can be beneficial to avoid hypothesis-driven models and instead take a data-driven approach. For this, we need a general purpose framework to handle the diverse data, and identify relationships across space, time, and modalities in a computationally efficient manner. In this work, we introduce such a framework. Our model, called the Neuroformer, can simulate neuronal populations, and jointly process an arbitrary number of additional modalities

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this drift with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) and current source density (CSD) as well as spikes. Second, we show that the CSD-based approach enables subsecond drift tracking. Third, we introduce an efficient online

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. The main meeting was held in Montreal followed by post-conference workshops in Mont-Tremblant, fostering intensive discussions and collaboration.

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 stil

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-

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-

Identifying how brain regions work together to process inputs and generate behavior is a central goal in systems neuroscience. Recently, there has been a surge of interest in characterizing communication between brain regions and shared variability in multi-region neural recordings. In this work, we use a multi-region latent variable model (LVM) to identify computations shared-across and independent-to specific brain regions in spiking neural population data from two different animals. Here, we use a multi-region latent variable model (LVM) to identify computations shared-across and independen

A fundamental question in systems neuroscience is how populations of neurons represent sensory, motor, and cognitive variables. Yet how these neural representations evolve over slow timescales is not well understood. Recent work has proposed using tensor decomposition methods to identify low-dimensional latent dynamics without requiring trial-averaging (tensor component analysis; TCA). This approach assumes that the data tensor can be described as a sum of components with fixed neural weights and temporal dynamics that vary over trials only in amplitude. However, recent evidence suggests that

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. 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. The workshops feature in-depth discussion of current topics of interest in a small group setting.

Characterizing perceptual decision-making is an important goal of systems neuroscience. Recent work has shown that animal decision-making behavior is not stationary, but exhibits switches between discrete latent states within a single experimental session. However, fitting these complex models requires large amounts of data, a crucial impediment to progress. Here we propose to overcome this obstacle by introducing active learning methods for discrete latent variable models of decision-making. Active learning seeks to improve the efficiency of experiments by selecting highly informative stimul

Establishing accurate as well as interpretable models of neural networks activity is an open challenge in systems neuroscience. Here we infer an energy-based generative network model of the anterior rhombencephalic turning region (ARTR) of zebrafish larvae using calcium-imaging recordings of the spontaneous activity of hundreds of neurons. While our data-driven model is trained to solely reproduce the short-term statistics of the neural activity, its dynamics exhibits persistence on much longer time scales. The model's persistence time decreases with water temperature in agreement with neurona

Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience. Sensory-driven decision making, in particular, has been shown to involve context-dependent processing in multiple brain regions, but a precise description of the interactions between regions remains an open problem. Addressing this problem requires new methods for inferring multi-region activity that account for sensory inputs and communication between brain regions. Here we develop multi-region switching state space models with higher-order autoregressive dynamics, allowing for