dual-timescale-neuron-astrocyte-memory

name	dual-timescale-neuron-astrocyte-memory
description	Dual-timescale memory mechanism in spiking neuron-astrocyte networks for efficient navigation. Models complementary learning systems: long-term memory via astrocytic regulation + short-term suppression via spike-frequency adaptation. Use for neuromorphic navigation, spatial memory SNNs, bio-inspired RL, and partial observability tasks. Triggers: dual-timescale, neuron-astrocyte, spatial memory, navigation SNN, astrocyte memory, complementary learning, 双时程记忆, 星形胶质细胞
version	1.0.0
metadata	{"hermes":{"source_paper":"Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation (arXiv:2604.15391)","tags":["snn","astrocyte","navigation","memory","spiking","neuromorphic"]}}

Dual-Timescale Memory in Spiking Neuron-Astrocyte Network

Overview

Biological agents combine long-term memory of successful actions with short-term suppression of recently visited locations. This skill implements a bio-inspired dual-timescale memory system using spiking neuron-astrocyte networks for efficient navigation under partial observability.

Core Architecture

Two Complementary Memory Timescales

1. Short-term memory (STM): Spike-frequency adaptation (SFA)

   • Temporarily suppresses recently activated neurons
   • Prevents revisiting recent locations (~seconds scale)
   • Acts as "working memory" for exploration

2. Long-term memory (LTM): Astrocytic calcium dynamics

   • Astrocytes detect neural activity patterns via IP3 signaling
   • Modulate synaptic weights through gliotransmitter release
   • Consolidates successful navigation paths (~minutes+ scale)

Key Mechanism

• SFA: Adaptive threshold increases after spiking, decays exponentially
• Astrocyte: IP3 accumulation → calcium release → synaptic modulation
• Interaction: STM guides exploration; LTM consolidates learned paths

Implementation Pattern

import numpy as np

class DualTimescaleSNN:
    def __init__(self, n_neurons, tau_sfa=50, tau_astro=500):
        self.n = n_neurons
        self.tau_sfa = tau_sfa   # Short-term adaptation time constant
        self.tau_astro = tau_astro  # Long-term astrocyte time constant
        
        # State variables
        self.adaptation = np.zeros(n_neurons)  # STM: spike-frequency adaptation
        self.astrocyte = np.zeros(n_neurons)   # LTM: astrocytic calcium
        self.threshold = np.ones(n_neurons)    # Adaptive thresholds
        
    def step(self, input_current, dt=1.0):
        # Update adaptation (short-term memory)
        self.adaptation *= np.exp(-dt / self.tau_sfa)
        
        # Update astrocyte (long-term memory)
        self.astrocyte *= np.exp(-dt / self.tau_astro)
        
        # Adaptive threshold = base + adaptation - astrocyte modulation
        effective_threshold = 1.0 + self.adaptation - 0.5 * self.astrocyte
        
        # Spiking logic
        membrane = input_current
        spikes = (membrane > effective_threshold).astype(float)
        
        # Update states after spiking
        .adaptation +=  * spikes  
        .astrocyte +=  * spikes   
        
         spikes

Navigation Application

def navigate_with_dual_memory(agent, grid, start, goal, max_steps=200):
    pos = start
    visited = set()
    
    for step in range(max_steps):
        # Get local sensory input (partial observability)
        local_view = get_local_view(grid, pos)
        
        # SNN decides action
        input_current = encode_view(local_view)
        spikes = agent.step(input_current)
        action = decode_action(spikes)
        
        # Execute action
        new_pos = move(pos, action)
        
        # STM prevents immediate return (SFA suppression)
        # LTM reinforces successful paths (astrocyte modulation)
        
        if new_pos == goal:
            return True
        pos = new_pos
    
    return False

Advantages

• Efficient exploration: SFA prevents looping back to recent locations
• Path consolidation: Astrocyte dynamics reinforce successful routes
• Partial observability: Works without full environment map
• Energy efficiency: Sparse spiking + local computation
• Bio-plausibility: Matches complementary learning systems theory

Parameters

Parameter	Typical Range	Effect
tau_sfa	20-100 ms	STM decay speed
tau_astro	200-1000 ms	LTM consolidation speed
SFA_strength	0.1-0.5	Suppression intensity
Astro_modulation	0.1-0.3	LTM influence on threshold

Related Skills

• dual-timescale-memory-astrocyte
• dual-timescale-memory-spiking-neuron-astrocyte-network-efficient