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 learn 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 the circuit to adaptively whiten neural responses on
short timescales exclusively using gain modulation.

通过多时间尺度机制建立的自适应白化模型，使用突触可塑性和增益调节作为生物学适应性的基础，通过短时间尺度的增益调节，长时间尺度的突触学习来实现神经响应的自适应白化。