learning-sequence-timing-spiking-neurons

name	learning-sequence-timing-spiking-neurons
description	Learning sequence timing and control of replay speed in networks of spiking neurons — sTM model extension for encoding element-specific timing and flexible replay speed modulation via oscillatory background input. arXiv 2605.22523 (May 2026).
arxiv_id	2605.22523
published	2026-05-21T00:00:00.000Z
category	neuroscience
tags	["spiking-neural-network","sequence-learning","replay","timing","oscillations","sTM","temporal-memory"]
activation	spiking neural network, sequence timing, replay speed, sTM model, temporal memory

Learning Sequence Timing and Control of Replay Speed in Networks of Spiking Neurons

arXiv: 2605.22523 | Authors: Melissa Lober, Younes Bouhadjar, Markus Diesmann, Tom Tetzlaff
Affiliation: Jülich Research Centre, RWTH Aachen, Fraunhofer IIS

Overview

Processing sequential inputs is a fundamental brain function underlying sensory perception, language, and motor control. This paper extends the spiking Temporal Memory (sTM) model — a biologically inspired spiking neural network — to encode not just the order but the precise timing of sequence elements, and to flexibly control the speed of sequence replay.

Key Contributions

1. Timing Encoding via Delay Lines

• The sTM model discretizes time into elementary intervals shorter than dendritic plateau potentials (~100ms).
• Longer intervals are constructed by concatenating sequentially activated neuronal assemblies ("delay lines") within the same minicolumn.
• Produces a sparse "bar code" of neuronal activity encoding both element identity and temporal duration.
• Demonstrated on a musical melody (Roy Orbison's "Oh, Pretty Woman") with 8, 16, and 24-note sequences.

2. Oscillatory Background Input Controls Replay Speed

• Constant background input provides limited replay speed modulation.
• Oscillatory background input (simulating cortical theta/gamma rhythms) acts as a clock signal.
• Replay speed range: ~10-70 Hz, independent of encoding speed.
• Oscillation frequency, amplitude (50-200 pA), phase, and offset jointly determine replay characteristics.

3. Testable Predictions

• Elapsed time is encoded by unique, sparse spatiotemporal patterns of neural activity (not rate codes).
• Replay speed during wakefulness vs. sleep correlates with global oscillatory activity (EEG/LFP).
• Predicts cross-frequency coupling between replay speed and background oscillations.
• Spike threshold modulation by background inputs determines replay speed bounds.

Network Architecture (sTM Model)

• M=6 minicolumns, each with nE=200 excitatory + nI=1 inhibitory neurons
• Sparse random recurrent connectivity with dendritic action potentials (dAPs) as prediction signals
• Lateral inhibition → winner-take-all (WTA) competition
• Structural STDP (spike-timing-dependent plasticity) + continuous weight decay
• Plateau potentials last ~100ms, setting the intrinsic timescale

Methods

• Training: 500 presentations of melody sequences, fixed inter-element interval ΔT=40ms
• Slower tempo variants: repeating each note (1x → 2x → 3x) yielding C=8, 16, 24
• Replay mode: increased excitability (reduced spike threshold or background current)
• Background inputs: constant (Ī=50-300 pA) vs oscillatory (f=10-100 Hz, a=50-200 pA)
• Metric: stable replay with correct order, no spurious assembly activations

Key Results

1. Delay lines encode timing: Repeated same-stimulus presentations within a minicolumn generate sequentially activated assemblies encoding duration.
2. Oscillatory background > constant: Wider dynamic range, more robust replay, encoding-speed-independent.
3. Replay speed bounds: Lower bound ≈ 10Hz (dAP duration ~100ms), upper bound ≈ 70Hz (synaptic/membrane time constants).
4. Encoding-speed independence: Replay can be faster or slower than encoding speed without relearning.
5. Phase-sensitive: Oscillatory input phase relative to plateau onset affects reliability.

Implications

• Biologically plausible mechanism for representing both "what" and "when" in sequences.
• Links global brain oscillations (theta/gamma) to replay speed control — functional role for network rhythms.
• Relevant to memory consolidation (sharp-wave ripples, hippocampal replay), motor sequence learning, temporal processing in neurological disorders.
• Provides a SNN-based alternative to Transformer positional encodings for temporal structure.

Limitations

• Static minicolumn assignment — biological cortex has more flexible assembly formation.
• Single sequence demonstrated; multi-sequence interference not addressed.
• Assumes uniform inter-element intervals during encoding.
• Not validated on naturalistic variable-timing sequences.

Activation Keywords

spiking neural network, sequence timing, replay speed, sTM model, temporal memory, oscillatory control, dendritic action potential, working memory, sequence learning SNN, hippocampal replay

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