Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation.

翻译：早期感觉区的神经元通过归一化个体响应方差和降低响应间的相关性，快速适应不断变化的感知统计特征。这些转换可视为一种自适应形式的统计白化。现有自适应白化的机制模型仅单独使用突触可塑性或增益调节作为神经生物基础；然而，这两种模型各自存在显著局限性。本研究通过融合这两种方法，提出一种基于规范化的多时间尺度机制模型，该模型利用突触可塑性与增益调节的互补计算角色实现响应自适应白化。增益在快时间尺度上调整以适应当前统计情境，而突触则在慢时间尺度上被修改以匹配跨情境不变的输入统计结构特性。本模型源于一种新型多时间尺度白化目标函数，该函数将逆白化矩阵分解为对应突触权重的基向量与对应神经元增益的对角矩阵。我们在合成数据集与自然数据集上测试了该模型，发现突触在长时间尺度上学习到最优配置，使得短时间尺度上通过增益调节实现自适应白化。