| name | sleep-like-plasticity |
| description | Sleep-inspired homeostatic regularization for stabilizing spike-timing-dependent plasticity (STDP) in spiking neural networks. Uses sleep-like phases with modified dynamics to consolidate learning, prevent weight runaway, and improve generalization. Bio-inspired approach mimicking sleep-dependent memory consolidation in biological brains. Use for SNN training stability, continual learning, and bio-inspired regularization. Activation: sleep regularization SNN, homeostatic plasticity, sleep-like consolidation, STDP stabilization, offline consolidation, bio-inspired regularization |
Sleep-Based Homeostatic Regularization for STDP
Overview
Bio-inspired training technique that introduces sleep-like phases to stabilize STDP learning in spiking neural networks. During sleep phases, network dynamics are modified to consolidate important weights, prune weak connections, and prevent the weight explosion common in pure STDP.
Core Mechanism
Wake Phase (Learning)
- Standard STDP learning with input-driven activity
- Synaptic weights change based on spike timing correlations
- Network encodes task-relevant patterns
Sleep Phase (Consolidation)
- No external input; network runs with autonomous dynamics
- Homeostatic mechanisms adjust weights toward stability
- Synaptic scaling normalizes total synaptic strength
- Weak connections are pruned; strong connections are reinforced
Implementation Pattern
class SleepRegularizedSNN:
def __init__(self, n_neurons, tau_sleep=0.99, weight_floor=0.01):
self.n_neurons = n_neurons
self.weights = torch.randn(n_neurons, n_neurons) * 0.1
self.tau_sleep = tau_sleep
self.weight_floor = weight_floor
def wake_phase(self, input_spikes, stdp_lr=0.01):
pre_trace, post_trace = self.compute_traces(input_spikes)
delta_w = stdp_lr * (pre_trace - post_trace)
self.weights += delta_w
def sleep_phase(self, n_steps=100):
for _ in range(n_steps):
input_norm = self.weights.abs().sum(dim=0, keepdim=True)
target_norm = torch.ones_like(input_norm) * self.n_neurons
scale = target_norm / (input_norm + 1e-8)
self.weights *= scale
self.weights = self.weights * self.tau_sleep + (1 - self.tau_sleep) * 0.1
self.weights[self.weights.abs() < self.weight_floor] = 0
Key Benefits
- Stability: Prevents weight runaway in STDP
- Generalization: Sleep phases reduce overfitting
- Energy efficient: Sleep phases require no input data
- Biological plausibility: Mirrors sleep-dependent consolidation
- Continual learning: Sleep helps integrate new and old memories
References
- Andreas Massey, Aliaksandr Hubin, Stefano Nichele, Solve Sæbø, "Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks", arXiv:2601.08447v1, 2026-01-13
