dual-timescale-memory-snn-astrocyte

name: dual-timescale-memory-snn-astrocyte description: "Dual-timescale memory in spiking neuron-astrocyte networks for efficient navigation. Combines long-term memory of successful actions with short-term suppression of recently visited locations using astrocyte-mediated modulation. Use when implementing bio-inspired navigation, working memory in SNNs, or astrocyte-neuron interactions. Triggers: dual timescale memory, astrocyte SNN, spiking navigation, neuron-astrocyte network, efficient exploration." version: 1.0.0 author: Research Synthesis license: MIT metadata: hermes: source_paper: "Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation (arXiv:2604.14487)" citations: 0 published: "2026-04-15" tags: - spiking-neural-networks - astrocyte - dual-timescale - memory - navigation - neuromodulation

Dual-Timescale Memory in Spiking Neuron-Astrocyte Networks

Overview

This methodology implements dual-timescale memory mechanisms inspired by biological neuron-astrocyte interactions for efficient spatial navigation and exploration.

Based on the paper "Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation" (arXiv:2604.14487, 2026-04-15).

Core Principles

Long-term memory (LTM): Stores successful action outcomes via synaptic plasticity (STDP)
Short-term memory (STM): Suppresses recently visited locations via astrocyte-mediated calcium dynamics
Neuromodulatory coupling: Astrocytes modulate neuronal excitability based on recent activity history

Mathematical Framework

Astrocyte Calcium Dynamics

d[Ca2+]/dt = -[Ca2+]/τ_ca + α · Σ δ(t - t_spike)

Where:

τ_ca: Calcium decay time constant (~1-10 seconds)
α: Calcium influx per spike
t_spike: Spike times of connected neurons

Dual-Memory Integration

I_modulation = β_LTM · w_syn + β_STM · f([Ca2+])

Where:

w_syn: Synaptic weight (LTM component)
f([Ca2+]): Astrocyte-mediated suppression (STM component)
β_LTM, β_STM: Scaling factors for each timescale

Implementation

class AstrocyteModulatedSNN:
    def __init__(self, num_neurons, tau_ca=5.0, alpha=0.1):
        self.tau_ca = tau_ca  # Astrocyte calcium time constant
        self.alpha = alpha    # Calcium influx per spike
        
    def update_calcium(self, spikes, dt):
        """Update astrocyte calcium concentration."""
        dca = -self.ca_conc / self.tau_ca + self.alpha * spikes.sum()
        self.ca_conc += dca * dt
        return self.ca_conc
    
    def modulation_current(self, calcium, synaptic_weights):
        """Compute astrocyte-mediated modulation."""
        ltm = synaptic_weights  # Long-term memory
        stm = torch.exp(-calcium)  # Short-term suppression
        return ltm + stm
    
    def forward(self, spikes, dt):
        calcium = self.update_calcium(spikes, dt)
        modulation = self.modulation_current(calcium, self.weights)
        output = self.neuron_update(spikes, modulation)
        return output

Applications

Autonomous robot navigation
Spatial memory systems
Exploration-exploitation tradeoff
Neuromorphic edge AI
Bio-inspired SLAM

Activation Keywords

dual timescale memory, astrocyte SNN, spiking navigation, neuron-astrocyte network, efficient exploration, calcium dynamics

Related Skills

snn-learning-rules-dynamics - SNN learning fundamentals
working-memory-heterogeneous-delays - Delay-based working memory
neuromorphic-low-power-ai - Neuromorphic computing