SYNC26: Spatiotemporal dYnamics in Neural Computation

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. We hypothesize that combining computational modeling with real-data analysis will reveal distinct neural dynamics underlying normal as well as violation conditions, shedding light on how the brain transitions between expected and unexpected states.

Projects

1. Modeling and interpreting neural traveling waves: Develop models that reproduce cortical traveling waves and test how they support distributed computation and routing.

2. 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.

3. Adaptive thresholds in delta-sparse recurrent networks: Explore if adaptive thresholding improves efficiency.

4. Phase-coded representation and reasoning in AI: Investigate how phase-based coding could be introduced into large language models.

5. Marker identification on real data: Analyze electrophysiological recordings to identify wave and control markers.

6. 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

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

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

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