activity-regeneration-transient-synaptic-memory

name	activity-regeneration-transient-synaptic-memory
description	A minimal neuronal network model with finite-lifetime synapses to study activity regeneration from silent states via transient synaptic memory. Use when modeling neuronal network dynamics, short-term memory, or silent-state reactivation.
metadata	{"arxiv_id":"2607.14000","authors":["Mozhgan Khanjanianpak","Alireza Valiadeh"],"subjects":["Neurons and Cognition (q-bio.NC)","Disordered Systems and Neural Networks (cond-mat.dis-nn)","Statistical Mechanics (cond-mat.stat-mech)"]}

Activity Regeneration from Transient Synaptic Memory Skill

This skill implements the model from arXiv:2607.14000 for studying activity regeneration in neuronal networks with transient synaptic memory.

Core Methodology

The model introduces a minimal neuronal network with finite-lifetime synapses and investigates the mechanism underlying spontaneous activity regeneration following complete neuronal silence.

Key findings:

• The residual synaptic configuration at the first silent state determines whether network activity terminates after a single activation cycle or spontaneously regenerates an additional cycle.
• The Latent Excitatory Recruitment (LER) capacity, quantified by the cumulative number of fresh excitatory neurons, is a near-perfect predictor of multi-cycle dynamics.
• Distinct dynamical outcomes emerge in an otherwise homogeneous neuronal network, demonstrating that transient synaptic memory alone is sufficient to generate diverse future dynamics.

Implementation Steps

1. Define the Neuronal Network Model with Finite-Lifetime Synapses

# Define neuronal and synaptic dynamics
def neuronal_dynamics(V, I_syn, I_ext):
    # Example: integrate-and-fire neuron
    dVdt = (-V + R*I_syn + I_ext) / tau_m
    return dVdt

def synaptic_dynamics(s, t, tau_s):
    # Synaptic variable with exponential decay
    dsdt = -s / tau_s
    return dsdt

# Finite-lifetime synapses: synapses have a lifetime after which they are reset
def update_synapses(synapses, t, lifetime):
    # Remove synapses older than lifetime
    active_synapses = [syn for syn in synapses if t - syn['birth_time'] < lifetime]
    return active_synapses

2. Simulate Network Activity and Silent States

def simulate_network(N, T, stimulus_duration):
    # Initialize neurons and synapses
    # Apply stimulus for stimulus_duration
    # Then let network evolve in silence
    # Record activity and synaptic states
    pass

3. Compute Latent Excitatory Recruitment (LER) Capacity

def calculate_LER(synaptic_states):
    # LER: cumulative number of fresh excitatory neurons that can be recruited
    # from the silent state synaptic configuration
    return sum([syn['weight'] for syn in synaptic_states if syn['type'] == 'excitatory' and syn['is_fresh']])

4. Predict Future Dynamics from Silent State Synaptic Configuration

def predict_future_activity(silent_state_synapses):
    ler = calculate_LER(silent_state_synapses)
    if ler > threshold:
        return "activity_regeneration"
    else:
        return "activity_termination"

Validation

Simulations should reproduce:

• Activity termination after a single activation cycle for low LER
• Spontaneous activity regeneration for high LER
• The near-perfect predictive power of LER for multi-cycle dynamics

Resources

scripts/

• simulate_network.py - Simulation of the neuronal network with transient synapses
• calculate_ler.py - Calculation of Latent Excitatory Recruitment capacity
• predict_dynamics.py - Prediction of future activity from silent state

references/

• ornstein_uhlenbeck_process.md - Mathematical details of the neuronal substrate model (if needed)
• finite_lifetime_synapses.md - Model of synapses with finite lifetime

assets/

• network_diagram.png - Diagram of the neuronal network model
• ler_vs_activity.png - Plot showing LER vs. activity regeneration

Activation Keywords

• activity-regeneration-transient-synaptic-memory
• transient synaptic memory
• silent state reactivation
• latent excitatory recruitment
• neuronal network dynamics

Validation

After implementing this skill, verify that:

1. The model shows activity termination for low LER and regeneration for high LER.
2. LER is a near-perfect predictor of multi-cycle dynamics.
3. The silent state synaptic configuration contains sufficient information to predict future evolution.

References

Khanjanianpak, M., & Valiadeh, A. (2026). Activity Regeneration from Silent States in Neuronal Networks with Transient Synaptic Memory. arXiv preprint arXiv:2607.14000.

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