Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Proc Natl Acad Sci U S A. 2022 Jan 11;119(2). doi: 10.1073/pnas.2023340118.
Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, -1}) (i.e., an "ON-minus-OFF" approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness.
不变刺激识别是所有感觉系统都必须应对的具有挑战性的模式识别问题。由于刺激引起的神经反应会受到多种方式的干扰，那么这种计算能力是如何实现的呢？我们在蝗虫嗅觉系统中研究了这个问题。我们发现，在有吸引力的条件下进行训练的蝗虫能够稳健地识别出训练过的气味，而不受刺激持续时间、动态或历史的变化，或背景和环境条件的变化的影响。然而，个体和群体水平的神经反应会不可预测地随许多这些变化而变化。我们的结果表明，线性统计解码方案，即对 ON 神经元赋予正权重，对 OFF 神经元赋予负权重，解决了神经变异性和行为稳定性之间的这种明显混淆。此外，使用仅具有三元权重（{+1、0、-1}）（即“ON 减去 OFF”方法）简化解码器不会影响性能，从而在简单性和鲁棒性之间取得了很好的平衡。
