Heterogeneous quantization regularizes spiking neural network activity.

ABSTRACT: The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a capacity mediated by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities using a plastic network that requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy, based on the biological system architecture, that normalizes and quantizes analog data into spike-phase representations

SUBMITTER: Moyal R 

PROVIDER: S-EPMC12019593 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature