Heterogeneous "cell types" can improve performance of deep neural networks

Deep convolutional neural networks (CNNs) are powerful computational tools for a large variety of tasks (Goodfellow, 2016). Their architecture, composed of layers of repeated identical neural units, draws inspiration from visual neuroscience. However, biological circuits contain a myriad of additional details and complexity not translated to CNNs, including diverse neural cell types (Tasic, 2018). Many possible roles for neural cell types have been proposed, including: learning, stabilizing excitation and inhibition, and diverse normalization (Marblestone, 2016; Gouwens, 2019). Here we investigate whether neural cell types, instantiated as diverse activation functions in CNNs, can assist in the feed-forward computational abilities of neural circuits. Our heterogeneous cell type networks mix multiple activation functions within each activation layer. We assess the value of mixed activation functions by comparing image classification performance to that of homogeneous control networks with only one activation function per network. We observe that mixing activation functions can improve the image classification abilities of CNNs. Importantly, we find larger improvements when the activation functions are more diverse, and in more constrained networks. Our results suggest a feed-forward computational role for diverse cell types in biological circuits. Additionally, our results open new avenues for the development of more powerful CNNs.