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Topic leaders
Topic leaders
Co-organizers
Co-organizers
Invited speakers
Invited speakers
Oscillatory processes in functional neural models
Bi-directional predictive coding
Phase codes in cognition
Neuromodulation of recurrent circuits
Continuous time state space models
BCI using oscillatory features
Goals
Goals
To explore how neural dynamics underlie cognition, memory, and perception. This interdisciplinary topic invites researchers from neuroscience, machine learning, applied math, physics and cognitive science to examine how spatiotemporal coordination in neural populations supports communication, gating, temporal binding, and distributed computation; and further, how analogous principles might be leveraged in AI.
What to expect
What to expect
You'll attend tutorials and talks, pick up new technical skills, and, most importantly, brainstorm ideas, team up on projects, and show off your work at the public open house demo day. You'll also meet and collaborate with students, postdocs, and researchers from around the world. But Telluride isn't all work; there will also be time for mountain hikes and evenings at the local pub.
Projects
Projects
Modeling and interpreting neural traveling waves: Develop models that reproduce cortical traveling waves and test how they support distributed computation and routing.
Wave-based state-space models for motor control: Apply wave SSMs to motor control tasks to investigate how oscillatory dynamics contribute to planning and adaptive feedback.
Adaptive thresholds in delta-sparse recurrent networks: Explore if adaptive thresholding improves efficiency.
Phase-coded representation and reasoning in AI: Investigate how phase-based coding could be introduced into large language models.
Marker identification on real data: Analyze electrophysiological recordings to identify wave and control markers.
Oscillation-based brain–computer interfaces: Design BCIs that decode intent, speech, or movement from oscillatory features (e.g., beta/gamma rhythms, ERPs), focusing on marker extraction and real-time closed-loop control.
Materials and Equipment
Materials and Equipment
Pre-workshop Preparation and Education
Jupyter notebooks with analysis pipelines for public datasets of neural recordings
Training scripts for small SSMs and wave RNNs
Compilation of relevant datasets and papers for projects
Available Hardware
Robots: AgileX PiPer, rover with torque feedback and camera system, arm with a gripper, several small quadrupeds
Recording equipment (in collaboration with BrainVision and the organizers of the auditory topic): EEG, pupillometry headset, EMG, fNIRS
Equipment for Tutorials: Human SpikerBox and claw neuroprosthetic (Backyard Brains)
Talks and Tutorials
Talks and Tutorials
Tentative talks and tutorials include:
Simulating Spiking State Space Models
Introduction to Electrophysiological Signals and Analysis Methods
Introduction to Dynamic Neural Fields (DNF)
Travelling Waves as a Biologically Plausible Context
Advanced Methods in Electrophysiological Signal Analysis
Dynamics in Predictive Coding Networks
The Thermodynamics of Mind
Auditory Modelling using Bayesian Inference
Transformers and Cortical Waves
Recommended reading
Recommended reading
Travelling waves and oscillations for computation
Jacobs, M., Budzinski, R. C., Muller, L., Ba, D., & Keller, T. A. (2025). Traveling Waves Integrate Spatial Information Through Time. [Link]
Keller, T. A., & Welling, M. (2023, July). Neural wave machines: learning spatiotemporally structured representations with locally coupled oscillatory recurrent neural networks. [Link]
Muller, L., Chavane, F., Reynolds, J., & Sejnowski, T. J. (2018). Cortical travelling waves: mechanisms and computational principles. [Link]
Budzinski, R. C., Busch, A. N., Mestern, S., Martin, E., Liboni, L. H., Pasini, F. W., ... & Muller, L. E. (2024). An exact mathematical description of computation with transient spatiotemporal dynamics in a complex-valued neural network. [Link]
Liboni, L. H., Budzinski, R. C., Busch, A. N., Löwe, S., Keller, T. A., Welling, M., & Muller, L. E. (2025). Image segmentation with traveling waves in an exactly solvable recurrent neural network. [Link]
Energy-based models and predictive coding
Oliviers, G., Tang, M., & Bogacz, R. (2025). Bidirectional predictive coding. [Link]
Biological recurrent neural networks
Prince, L. Y., Eyono, R. H., Boven, E., Ghosh, A., Pemberton, J., Scherr, F., ... & Wilmes, K. A. (2021). Current state and future directions for learning in biological recurrent neural networks: A perspective piece. [Link]
Spatial organization in models
Rathi, N., Mehrer, J., AlKhamissi, B., Binhuraib, T., Blauch, N. M., & Schrimpf, M. (2024). TopoLM: brain-like spatio-functional organization in a topographic language model. [Link]
Keywords: spiking neural networks; recurrent neural networks; brain–computer interfaces; state space models; neural oscillations; travelling waves; energy-based models; predictive coding; neuromodulation
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