Catastrophic forgetting represents a significant challenge in neuromorphic computing, where neural networks lose previously acquired knowledge when trained on new tasks. This phenomenon arises due to the inherent plasticity of synaptic weights, which are overwritten during learning processes. Neuromorphic systems, designed to mimic biological neural networks, must balance stability (retention of learned information) and plasticity (ability to learn new tasks) to function effectively in dynamic environments.
Biological neural networks employ multiple forms of synaptic plasticity to maintain stability while adapting to new information:
EWC identifies and protects important weights for previous tasks by calculating their Fisher information, effectively creating an importance map that constrains learning of new tasks.
SI tracks the contribution of each synapse to the reduction in loss function during training, using this accumulated intelligence to protect crucial connections during subsequent learning.
These approaches maintain a subset of previous training examples or generate synthetic samples to interleave with new task learning, providing regular reminders of past knowledge.
The integration of multiple plasticity rules offers a promising solution to catastrophic forgetting by providing complementary stability mechanisms:
Recent implementations demonstrate that pairing Hebbian learning with homeostatic scaling can maintain network stability across sequential learning tasks. The Bienenstock-Cooper-Munro (BCM) rule provides one such biologically-inspired framework.
Short-term plasticity (STP) acts as a temporary buffer for new information while long-term potentiation/depression (LTP/LTD) mechanisms consolidate important changes:
Recent research proposes synaptic models with multiple dynamic variables operating at different temporal scales:
| Variable | Time Constant | Function |
|---|---|---|
| Fast | 10-100ms | Rapid learning of new patterns |
| Slow | Hours-days | Long-term memory retention |
| Structural | Days-years | Stable knowledge representation |
The practical realization of hybrid plasticity mechanisms faces several technical hurdles:
Accurate implementation of multiple plasticity rules demands:
Additional plasticity mechanisms increase energy consumption per synaptic operation, requiring careful optimization to maintain neuromorphic efficiency advantages over conventional computing.
The TrueNorth architecture implemented a combination of reward-modulated STDP and homeostatic scaling, demonstrating improved sequential learning capabilities while maintaining 70mW power consumption for 1 million neurons.
Loihi 2 incorporates programmable synaptic learning rules that can simultaneously implement STDP and homeostatic plasticity, enabling investigation of hybrid approaches in a scalable neuromorphic system (up to 1 million neurons per chip).
This mathematical approach evaluates whether a learning system will converge to stable states despite ongoing plasticity, providing formal guarantees against catastrophic forgetting.
Recent work models the interaction of different plasticity rules as competing flows in synaptic weight space, offering geometric insights into their combined effects on network dynamics.
Emerging approaches explore context-dependent switching between plasticity rules based on task demands or internal state monitoring.
Advanced architectures may implement different plasticity mixtures across network layers, matching rule combinations to each layer's functional role in information processing.
The research community has developed several benchmark suites to evaluate catastrophic forgetting mitigation strategies:
Effective evaluation requires multiple complementary measures:
| Approach | Retention Improvement (%) | Computational Overhead | Hardware Feasibility |
|---|---|---|---|
| EWC-only | 45-60 | Moderate (quadratic in params) | Challenging for large nets |
| SI-only | 50-65 | Low (linear in params) | Good for analog impl. |
| Hybrid STDP+Homeostasis | 60-75 | Moderate-High | Requires multi-timescale synapses |
| Dual-network architectures | 70-85 | High (2x params) | Chip area intensive |