Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Proc Natl Acad Sci U S A. 2022 Feb 1;119(5). doi: 10.1073/pnas.2108882119.
To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response's amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.
为了探索神经回路如何表示新颖和熟悉的输入，我们向小鼠呈现了重复的图像集，其中稀疏地替换了新图像。使用双光子钙成像技术记录小鼠初级视觉皮层的 2/3 层神经元的活动，我们发现新颖的图像在大多数神经元中引起了过量的活动。这种新颖性反应迅速出现，其时间常数为 2.6±0.9s。当一组新图像被重复呈现时，大多数神经元在前几次呈现时都有类似的高活性，这种活性会衰减到稳定状态，时间常数为 1.4±0.4s。当我们增加图像集的数量时，新颖性反应的幅度会降低，在我们的条件下，这可以存储约 15 个熟悉的图像。使用具有个体调谐和增益控制的突触前神经元的自适应亚基模型可以对这些结果进行定量解释。该结果表明，局部神经回路可以使用通用的、广泛可用的机制为新颖和熟悉的输入创建不同的表示。
