Topic: systems neuroscience

The annual Computational and Systems Neuroscience (Cosyne) meeting, an exchange forum for experimental and theoretical/computational approaches to problems in systems neuroscience. Cosyne returns to Montreal, Canada, from 11-16 March 2027.

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 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

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-

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.

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 o

Open-source tools are gaining an increasing foothold in neuroscience. The rising complexity of experiments in systems neuroscience has led to a need for multiple parts of experiments to work together seamlessly. This means that open-source tools that freely interact with each other and can be understood and modified more easily allow scientists to conduct better experiments with less effort than closed tools. Open Ephys is an organization with team members distributed all around the world. Our mission is to advance our understanding of the brain by promoting community ownership of the tools w

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

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

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.

Recurrent neural networks (RNNs) are proving increasingly useful in systems neuroscience. Training RNNs on cognitive-like tasks has demonstrated their potential to infer dynamical processes and computations in the brain [1, 2]. They have additionally been shown to provide an interpretable link between connectivity structure and activity dynamics in low-rank networks [3]. However, a potentially serious shortcoming is that many (if not most) of these networks disobey biological constraints in terms of their connectivity structure and response properties, which raises questions as to their interp

A primary goal in systems neuroscience is to understand how neuronal connectivity impacts the activity and dynamics in mechanistic models of neural processing. New connectome datasets of the fruit fly provide access to the connectivity of every neuron in its neural circuits. However, the functional role of recurrent connections in the fruit fly remains incompletely understood. For instance, although its visual system is predominantly feedforward, strong feedback connections have been identified in its motion detecting pathways [1]. What is the role of recurrent connections in the visual percep

A central question in systems neuroscience is what features of neural networks determine their memory capacity and whether these features are optimized in the brain. Here, we provide an analytical estimate of the memory capacity for a general class of network models. Our derivation extends previous theoretical results [1], which assumed homogeneous connectivity and coding levels (i.e., cell activation probabilities in the memory patterns), to models with arbitrary network architectures (i.e., different constraints on the arrangement of connections between cells) and heterogeneous coding levels