Laconic Neuromorphic Vision

In this paper, we introduce laconic neuromorphic vision, a novel visual intelligence system that encodes scene dynamics into sparse spike representations and processes them using spiking neural networks, mimicking the sparse activation patterns of biological neurons. This approach drastically reduces the input data required while maintaining high accuracy in image classification. Experiments on the MNIST and Fashion-MNIST datasets demonstrate that our system achieves comparable results to traditional methods with significantly fewer input pixels and spikes. This work provides a fresh perspective on energy-efficient, biologically inspired vision systems, with potential applications in edge computing and autonomous systems. Code and data will be released upon manuscript acceptance.