The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity

arXiv:2609.26940 · cs.NE, cs.LG, q-bio.NC · Submitted 2026-09-22 · Read on arXiv

cs.NE, cs.LG, q-bio.NC

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/fangwei123456/spikingjelly

Terminology

Accuracy — 24× in this paper · explained in 1 episode
Regularization — 18× in this paper · explained in 3 episodes
Sparsity — 14× in this paper · explained in 4 episodes
Alignment — 5× in this paper · explained in 1 episode
Expressive Leaky Memory — 5× in this paper
In International Conference on Learning Representations — 4× in this paper
Spiking Heidelberg Digits — 3× in this paper
Transformer — 3× in this paper · explained in 2 episodes
Generalization — 2× in this paper · explained in 13 episodes
Resource Constraints — 2× in this paper · explained in 3 episodes
Trace — 2× in this paper · explained in 2 episodes
Abstraction — 1× in this paper · explained in 1 episode
Clustering — 1× in this paper · explained in 2 episodes
Conference on Computer Vision and Pattern Recognition — 1× in this paper
Core — 1× in this paper · explained in 2 episodes
Dynamic Vision Sensor — 1× in this paper
Energy Efficiency — 1× in this paper · explained in 3 episodes
In International Conference on Machine Learning — 1× in this paper
Sample Complexity — 1× in this paper · explained in 15 episodes
Sample Efficiency — 1× in this paper · explained in 4 episodes
Scaling Laws — 1× in this paper · explained in 4 episodes

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