Redwood Center for Theoretical Neuroscience, Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720, USA.
J Neurosci. 2013 Mar 27;33(13):5475-85. doi: 10.1523/JNEUROSCI.4188-12.2013.
Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.
自然场景的稀疏编码模型可以解释初级视觉皮层（V1）的几个生理特性，包括简单细胞感受野（RF）的形状和 V1 神经元高度峰态的发放率。目前用于模式学习和稀疏编码的尖峰网络模型需要兴奋性简单细胞之间的直接抑制性连接，这与兴奋性（谷氨酸能）和抑制性（GABA 能）神经元之间的生理区别（戴尔定律）相冲突。同时，抑制性神经元在皮质微电路功能中的计算作用尚未得到充分解释。在这里，我们表明，向 V1 的尖峰模型添加单独的抑制性神经元群体可以符合戴尔定律，为至少一类中间神经元提出了计算作用，并解释了 V1 中的某些观察到的生理特性。当在自然图像上进行训练时，这个兴奋性-抑制性尖峰电路使用仅基于局部突触可塑性规则，学习到具有类似于 V1 中发现的 Gabor 型 RF 的稀疏码。抑制性神经元通过抑制可预测的尖峰来实现稀疏码形成，从而主动去相关兴奋性群体。该模型预测，与兴奋性细胞相比，只需要相对较少的抑制性细胞，并且兴奋性和抑制性输入应该相关，这与视觉皮层的实验发现一致。我们还引入了一种新的局部学习规则，用于测量神经元之间与刺激相关的相关性，以支持神经编码中的“解释消除”机制。
