Topic: neural dynamics

GoEMMI (GOettingen EMergent Minds) is a three-week winter school on learning and computation in brains and machines, held from 15 February to 6 March 2027 at the Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, Göttingen. The programme is organised around three blocks: the theory of emergence and computation, covering conceptual and mathematical frameworks for emergent macrovariables, dynamical autonomy, compositionality and information-theoretic approaches to computation and causality; learning in brains and machines, covering learning objectives, representation formati

The Cognitive Computational Neuroscience (CCN) conference connects cognitive science, neuroscience and artificial intelligence through computational explanations of complex behaviour. Its scope includes mechanistic interpretability, representation learning and alignment, biological and artificial intelligence, neural dynamics, and models of perception, cognition and action. The primarily single-track format combines keynotes, research talks and posters with some parallel community sessions. Generative Adversarial Collaborations examine competing theories; keynote-and-tutorial sessions connect

The Gonzalez Lab at UCSF is recruiting a research technician for interdisciplinary work on vocal communication, social behavior, sleep, and neural dynamics. The role combines bird behavior, high-density electrophysiology, optical imaging, closed-loop stimulation, custom instrument development, and Python or MATLAB analysis, with later opportunities to support human EEG and natural-speech studies.

The goal of the Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The Scientist will use the Institute's large-scale connectivity atlases and brain-wide recordings to understand how the brain's wiring organizes neural dynamics and drives flexible behavior. The position o

The Neural Dynamics accelerator at the Allen Institute studies how the brain generates flexible behavior, seeking the algorithms implemented by dynamics in brain-wide neural circuits. The Scientist I (ND - Molecular Anatomy) builds next-generation light microscopes, predominantly light-sheet systems, and uses them to image expanded tissue: designing, building, aligning and characterizing custom instruments, optimizing imaging of expanded specimens, preparing samples through tissue expansion and labeling, acquiring large datasets and analysing them for biological insight, documenting protocols

The Neural Dynamics accelerator at the Allen Institute studies how the brain generates flexible behavior. Reporting to the Institute's President and CEO, the Executive Vice President and Co-Director co-leads its scientific vision and long-range strategic plan and is the single point of accountability for people, administration and operations. The research agenda spans how cell groups form circuits that generate cognition and behavior; cell-type-specific monitoring and manipulation of spiking with tools such as Neuropixels probes; brain-wide connectivity between defined cell types; optical meas

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Tilo Schwalger discusses population-level descriptions of neural dynamics and how mean-field models can be derived from underlying network mechanisms.

Neural activity is often analyzed with respect to external referents, such as the onset of a sensory stimulus or an overt motor action. Simultaneous recordings allow referencing neurons’ activity to each other and thus detecting signals that are internal to the organism. Further, multi-region simultaneous recordings allow observing how these internal signals are coordinated across the brain. Following this logic in rats performing a perceptual decision-making task, we recorded simultaneously from thousands of neurons across up to 20 brain regions at once. Here we report two internal signals wh

Michael Breakspear and Mac Shine examine how neural dynamics change from individual cells to whole-brain systems. Their work connects gain modulation with the balance between integration and segregation, relates it to arousal pathways, and uses coarse-graining to investigate both scale-dependent coding and shared dynamical properties across neural populations.

Andrea Martin develops an account of language that combines compositional structure with physiological constraints from human brains. She discusses neural dynamics and low-dimensional manifolds as a way to connect statistical regularities with formal organization, and considers how this approach relates to the capabilities and limitations of large language models.

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations (Urai et al., 2022; Krakauer et al., 2017). In particular, neural latent embeddings can reveal underlying correlates of behavior, yet, we lack non-linear techniques that can explicitly and flexibly leverage joint behavior and neural data. Here, we fill this gap with a novel contrastive learning method that jointly uses time-series d

An emerging perspective in neuroscience connects neural dynamics, or the rules that govern how neural population activity develops over time, to functional computations performed by the brain [1]. Unfortunately, neural dynamics cannot be observed directly and must instead be inferred from neural recordings. One promising method to infer these dynamics is to use a latent dynamics model, commonly a Sequential Auto-Encoder (SAE), to reconstruct neural activity [2] with the assumption that better reconstruction performance corresponds to better dynamical accuracy. Unfortunately, most SAE readouts,

An open question in neuroscience is how the brain controls naturally generated movements. An increasing number of studies have shown that high-dimensional neural population dynamics lie within low-dimensional manifolds, which may be integral to neural control of behavior. However, many of these studies have focused on neural activity recorded during experimentally driven movements with little variability, leaving open the question of whether low-dimensional neural manifolds persist during naturalistic movements. In this paper, we investigate neural population dynamics in naturalistic reaching

Anesthesia is ubiquitous in hospitals, yet a mechanistic understanding of how anesthetic drugs induce unconsciousness is lacking. A prominent hypothesis suggests that dynamic stability is critical to cortical function: awake brains are poised at a state that is sufficiently excitable for activity generation and propagation, yet controllable and stable. Measuring dynamic stability during the transition from consciousness to anesthetic unconsciousness could identify the neural mechanisms disrupted by anesthesia, and thus provide insight into the neural basis of consciousness. Related work sugges

Learning in neural circuits manifests via slow changes in activity driving behaviour changes. Uncovering how the coordination of plasticity mechanisms found in the brain leads to such changes is a difficult problem, chiefly because we do not have access to the synaptic parameters underlying neural computation. Nevertheless, as neural population recording techniques improve and experiments track changes in neural dynamics over long durations, finer details about plastic changes could be inferred by observing and characterizing changes in neural activity. In this work, we present a step towards

A complete neuroscience requires multi-level theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. Unfortunately, we don't have cognitive models of behavior whose components can be decomposed into the neural dynamics that give rise to behavior, leaving an explanatory gap. Here, we decompose SUSTAIN, a clustering model of concept learning, into neuron-like units (SUSTAIN-d; decomposed). Instead of abstract constructs (clusters), SUSTAIN-d has a pool of neuron-like units. With millions of units, a key challenge is how to bridge from abstract c

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents com

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations. In particular, neural latent embeddings can reveal underlying correlates of behavior, yet, we lack non-linear techniques that can explicitly and flexibly leverage joint behavior and neural data. Here, we fill this gap with a novel method, CEBRA, that jointly uses behavioral and neural data in a hypothesis- or discovery-driven manne

For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers---efficient balanced spi

Motor adaptation is a widely-used paradigm for understanding short-term learning. However, it is unknown how existing skillsets acquired through the long-term learning process that begins in utero can affect motor adaptation. Long-term learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr

Trained artificial neural networks have become essential models in neuroscience. However, the robustness of these models to -- seemingly arbitrary -- design or initialization choices is currently debated. Some studies reported universality, others variability between solutions found by training. One choice, the magnitude of output weights, has recently received a lot of attention in machine learning, as it induces two very different classes of solutions in feed-forward networks. How this parameter affects solutions in recurrent neural networks (RNNs) trained on neuroscience tasks is not well

Individual neurons exhibit a rich repertoire of complex dynamical patterns which vary at fast timescales [1]. Despite these fast temporal dynamics, neural circuits are able to maintain stimulus representations over the course of behaviorally relevant timescales that can subsequently be decoded by time-invariant downstream circuits to perform motor actions. Previous studies have proposed that these observations can co-exist due to neural redundancy. Specifically, these frameworks suggest that task relevant information is stably stored in a low dimensional subspace [2][3]. Moreover, time invaria

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. Th