By name: learning-sequence-timing-replay-speed-snn description: "Learning sequence timing and control of replay speed in networks of spiking neurons. Extends the spiking Temporal Memory (sTM) model to encode element-specific duration and flexibly control replay speed via oscillatory background inputs. Applicable to computational neuroscience, SNN timing learning, neural sequence processing. Activation: spike timing, sTM model, sequence replay, oscillatory speed control, spiking temporal memory, neural sequence processing." user-invocable: true
Learning Sequence Timing and Replay Speed Control in SNNs
Source Paper: arXiv:2605.22523 - Learning sequence timing and control of replay speed in networks of spiking neurons
Authors: Melissa Lober, Younes Bouhadjar, Markus Diesmann, Tom Tetzlaff
Published: 2026-05-21
Core Methodology
1. Spiking Temporal Memory (sTM) Model Extension
The sTM model represents each sequence element by a small set of synchronously firing neurons, where the active set encodes the element's identity in its sequential context.
Key Innovation: Extends sTM from order-only to order-and-timing encoding:
- Duration encoding via sequential activation: Element duration is represented by sequential activation of element-specific neuronal populations
- Timescale flexibility: Enables encoding across a wide range of timescales
- Biologically plausible mechanism for learning and replaying complex temporal patterns
2. Oscillatory Clock Signal for Speed Control
Core Finding: Oscillatory background inputs serve as a clock signal providing robust and flexible speed control of sequence replay.
Mechanism:
- Global oscillatory activity (as observed in EEG/LFP) modulates replay speed
- Speed during wakefulness vs. sleep correlates with oscillatory characteristics
- Provides a neurophysiologically grounded mechanism for flexible timing
3. Sparse Spatiotemporal Encoding of Time
Key Insight: Elapsed time is encoded by unique and sparse spatiotemporal patterns of neural activity.
Key Contributions
- Timing learning: Biologically plausible mechanism for learning element-specific durations in spiking networks
- Speed control: Oscillatory background activity as a natural clock signal for replay speed modulation
- Implementation: Extends sTM without requiring additional biologically implausible mechanisms
Implementation Notes
Spiking Temporal Memory Architecture
- Each sequence element -> dedicated neuronal population with synchronous firing
- Duration -> sequential recruitment of within-element subpopulations
- Speed control -> oscillatory drive amplitude/frequency modulates activation dynamics
Key Parameters
- Element-specific timing: controlled by synaptic delays and recurrent connectivity within each population
- Oscillatory clock: frequency and amplitude determine replay speed
- Sparse coding: minimal overlap between representations of different elements
Related Skills
- [[spike-timing-neuronal-assemblies]] - STDP-based neuronal assembly formation
- [[working-memory-heterogeneous-delays]] - SNN working memory with delays
- [[cognisnn-brain-inspired-snn]] - Cognition-aware SNN architectures