Project: Synapse-X
Exploration of non-linear memory retention in transformer models. We addressed the "Catastrophic Forgetting" paradox in continuous learning streams.
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1.0 Abstract
Standard LLMs require full retraining to assimilate new knowledge clusters. Synapse-X introduces a "bi-cameral" architecture where a short-term volatility layer interacts with a long-term frozen core, mimicking the hippocampus-cortex relationship in mammals.
2.0 Methodology
We implemented a dynamic weight-decay algorithm (DWD) that identifies "redundant neurons" during sleep cycles (low traffic periods). These neurons are then repurposed for new data tokens without overwriting critical path logic.
def synapse_prune(weights, threshold=0.04):
for w in weights:
if w.activation_history < threshold:
w.reset() # Soft reset
w.plasticity = 1.0 # High learning rate
return optimize_network()
3.0 Results
Retention Rate
99.8%
GPU Load Reduction
-42%
Training Time
0.4s / epoch