Biologically Inspired Learning for Neural Networks
Author Information
Author(s): Wang Bo, Zhang Yuxuan, Li Hongjue, Dou Hongkun, Guo Yuchen, Deng Yue
Primary Institution: School of Astronautics, Beihang University, Beijing, China
Hypothesis
Can a spiking neural network (SNN) be enhanced by incorporating self-inhibiting autapse and neuron heterogeneity to improve learning and memorizing capacities?
Conclusion
The proposed HIFI model outperforms traditional SNNs in accuracy, efficiency, and latency while accurately identifying rare cell types in scRNA-seq data.
Supporting Evidence
- The HIFI model demonstrated a 1%–10% improvement in accuracy and a 17.83-fold reduction in energy consumption.
- HIFI was able to identify rare cell types associated with severe brain diseases that traditional SNNs could not.
- The model achieved superior performance on multiple AI benchmarks compared to existing SNNs.
Takeaway
This study created a new type of neural network that learns better by mimicking how real brain cells work, making it faster and more efficient.
Methodology
The study developed a Heterogeneous spIking Framework with self-Inhibiting neurons (HIFI) using a bi-level programming paradigm to optimize neuron-level and network-level parameters.
Limitations
The model's computational complexity may limit its application in resource-constrained environments.
Digital Object Identifier (DOI)
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