Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack
Lei Guo, Huan Liu, Yihua Song, Nancheng Ma
TL;DR
This paper introduces an fMRI-based spiking neural network that shows improved resistance to damage during speech recognition tasks.
Contribution
The novel fMRI-SNN model incorporates human brain topology to enhance damage resistance in spiking neural networks.
Findings
fMRI-SNN maintains high recognition accuracy even after 30% damage from stochastic attacks.
Synaptic plasticity and neural electrical activity are key to the model's damage resistance.
The brain-like topology significantly impacts the robustness of the network.
Abstract
Brain-like models are commonly used for pattern recognition, but they face significant performance degradation in neuromorphic hardware when exposed to complex electromagnetic environments. The human brain has adaptability to the exterior attack, and we expect that incorporating bio-plausibility into a brain-like model will enhance its robustness. However, brain-like models currently lack bio-plausibility. Therefore, we construct a spiking neural network (SNN) whose topology is constrained by human brain functional Magnetic Resonance Imaging (fMRI), called fMRI-SNN. To certify its damage resistance, we investigate speech recognition accuracy against stochastic attack. To reveal its damage-resistant mechanism, we explore the neural electrical features, adaptive modulation of synaptic plasticity, and topological features against stochastic attack. Research shows that fMRI-SNN surpasses…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural dynamics and brain function · Ferroelectric and Negative Capacitance Devices