snn-sequence-timing-replay

name	snn-sequence-timing-replay
description	Biologically plausible spiking neural network model for learning sequence timing and controlling replay speed. Extends the spiking Temporal Memory (sTM) model with element-specific duration encoding via sequential activation of neuronal populations, and uses oscillatory background inputs as a clock signal for flexible speed control. Use when working with: spiking neural networks for sequence learning, temporal memory models, sequence replay in SNNs, timing encoding in neural populations, oscillatory control of replay speed, STDP-based sequence learning, sleep replay mechanisms, hippocampal replays.
arxiv_id	2605.22523
published	2026-05-21
authors	Melissa Lober, Younes Bouhadjar, Markus Diesmann, Tom Tetzlaff
tags	["spiking neural network","sequence learning","temporal memory","replay","STDP","oscillations","timing","sTM model","neuromorphic computing"]

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

arXiv:2605.22523 (Lober, Bouhadjar, Diesmann, Tetzlaff, May 2026)
Category: q-bio.NC (Neurons and Cognition)

Core Idea

Sequences are fundamental to brain function (sensory perception, language, motor control). The spiking Temporal Memory (sTM) model learns sequence order but not precise timing. This paper extends sTM with:

1. Element-specific duration encoding — each sequence element activates a distinct chain of neuronal sub-populations, encoding both identity AND duration
2. Oscillatory speed control — background oscillations (like brain rhythms) serve as a clock to flexibly modulate replay speed, from slow (wakefulness) to fast (sleep)

Architecture

Standard sTM Model (Baseline)

• Each sequence element → synchronized burst from a small assembly of neurons
• Assembly identity encodes the element in its sequential context
• Spike-timing-dependent plasticity (STDP) learns order by strengthening excitatory connections between sequentially activated assemblies
• Inhibition prevents runaway excitation and enforces winner-take-all dynamics

Extended sTM with Timing (This Paper)

Duration encoding: Instead of each element activating a single assembly, the element activates a chain of assemblies in sequence. The length of the chain (number of sequential assemblies activated) encodes the element's duration.

• Short duration → short chain (few assemblies)
• Long duration → long chain (many assemblies)
• Each assembly in the chain fires for a fixed base interval; the total duration = chain length × base interval

Speed control via oscillations: Adding oscillatory background input to all neurons.

• High-frequency oscillations → shorter interspike intervals → faster chain traversal → faster replay
• Low-frequency oscillations → longer interspike intervals → slower chain traversal → slower replay
• The oscillation frequency globally modulates the speed of replay across all chains

Key Mechanisms

1. STDP-Based Assembly Formation

Pre-before-post: Δw = A⁺·exp(-Δt/τ⁺)   (potentiation)
Post-before-pre: Δw = A⁻·exp(-Δt/τ⁻)   (depression)

After learning, assemblies form: groups of neurons with strong recurrent excitatory connections that fire synchronously when activated. Each assembly is defined by its unique set of synaptic weights.

2. Chain Encoding of Duration

Element E1 (short):   A1 → A2
Element E2 (medium):  B1 → B2 → B3
Element E3 (long):    C1 → C2 → C3 → C4

Each assembly (A1, A2, B1, etc.) is a distinct group. The chain's length encodes duration. During learning, the chain structure emerges through STDP: when assembly A1 fires, it drives A2, which then drives A3, etc.

3. Oscillatory Clock Signal

Neurons receive a common oscillatory drive I_osc(t) = A·sin(2π·f·t). This modulates the membrane potential:

• Near threshold: oscillation determines WHEN the neuron fires
• Higher amplitude: tighter phase locking to oscillation
• Frequency modulation: changing f changes the timing of all spikes

4. Replay Speed Modulation

During recall, the same oscillatory input controls the speed:

Oscillation Frequency	Replay Speed	Biological Correlate
2–4 Hz (theta)	1× (slow)	Wakeful encoding, exploration
8–12 Hz (alpha)	1.5–2×	Relaxed wakefulness
150–250 Hz (sharp-wave ripples)	10–20× (fast)	Sleep consolidation, hippocampal replay

The replay speed is proportional to oscillation frequency across a wide range — the mechanism is robust and continuously tunable.

Key Results

1. Timing learned successfully: The model learns both the order AND duration of sequence elements purely through local plasticity rules (STDP).

2. Wide timescale range: Sequences with element durations spanning 10 ms to 1000 ms can be learned and replayed.

3. Oscillatory speed control: Replay speed varies linearly with oscillation frequency (verified over 5 Hz – 200 Hz range).

4. Biologically plausible: All mechanisms use only local learning rules (STDP) and biologically realistic inputs (oscillatory drive). No global error signal or supervisor.

5. Robust to noise: The mechanism works reliably with Poisson input noise and realistic synaptic failure rates.

Relation to Hippocampal Replay

Hippocampal replay during sleep (sharp-wave ripple events) compresses awake experiences 10-20×. This model provides a mechanistic explanation:

• During wakefulness: theta oscillations provide the clock → slow replay
• During sleep: sharp-wave ripples provide fast oscillations → compressed replay (10-20×)
• The SAME learned assembly chain supports both slow and fast replay — speed is determined by the oscillatory context, not by different synaptic strengths

Practical Implications

For Neuromorphic Computing

• Event-based sequence learning: SNNs naturally suited for temporal pattern learning
• On-chip speed control: A single oscillatory signal can globally modulate replay speed
• Power-efficient: Oscillation-controlled timing avoids per-neuron timer circuits

For Neuroscience

• Testable prediction: Elapsed time encoding via sequential assembly activation should be observable in hippocampal/temporal cortex recordings during sequence tasks
• Sleep replay mechanism: Oscillation frequency differences between theta and sharp-wave ripples explain replay speed differences

Activation Keywords

• spiking temporal memory
• sTM model
• sequence timing SNN
• replay speed modulation
• oscillatory clock neural
• STDP sequence learning
• temporal encoding spiking neurons
• hippocampal replay timing
• sharp-wave ripple compression
• chain assembly encoding