The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations; across sensory modalities, this capacity is aided 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. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights; this regularizes layer utilization, yoking total activity to the network's operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention.

翻译：从非规范输入中学习和识别物体特征一直是人工智能系统面临的长期挑战。大脑擅长在少量噪声观测样本下学习稳定的表征；跨感觉模态中，这种能力得益于基于领域知识的信号调理级联过程。嗅觉系统尤其需要解决一个由浓度变化、环境干扰和不可预测的相关传感器亲和性共同构成的源分离与去噪问题。为实现最优功能，其可塑性网络需要统计特性良好的输入。我们提出了一种数据无关的神经形态信号调理策略，通过该策略将模拟数据归一化并量化为脉冲相位表示。输入通过异构突触权重传递至复制的脉冲主神经元柱；这种方法规范了层利用率，将总活动量约束在网络运行范围内，并使内部表征对不受控的开放集刺激方差具有鲁棒性。我们通过增加数据感知的校准步骤扩展此机制，使量化权重的范围和密度能够适应累积的输入统计特性，通过平衡活动正则化与信息保留来优化资源利用率。