Deep learning (DL) has big-data processing capabilities that are as good, or even better, than those of humans in many real-world domains, but at the cost of high energy requirements that may be unsustainable in some applications and of errors, that, though infrequent, can be large. We hypothesise that a fundamental weakness of DL lies in its intrinsic dependence on integrate-and-fire point neurons that maximise information transmission irrespective of whether it is relevant in the current context or not. This leads to unnecessary neural firing and to the feedforward transmission of conflicting messages, which makes learning difficult and processing energy inefficient. Here we show how to circumvent these limitations by mimicking the capabilities of context-sensitive neocortical neurons that receive input from diverse sources as a context to amplify and attenuate the transmission of relevant and irrelevant information, respectively. We demonstrate that a deep network composed of such local processors seeks to maximise agreement between the active neurons, thus restricting the transmission of conflicting information to higher levels and reducing the neural activity required to process large amounts of heterogeneous real-world data. As shown to be far more effective and efficient than current forms of DL, this two-point neuron study offers a possible step-change in transforming the cellular foundations of deep network architectures.

翻译：深度学习（DL）在众多现实领域具备与人类相当甚至更优的大数据处理能力，但其高能耗特性可能在某些应用中难以为继，且虽属偶发却可能造成重大误差。我们假设，深度学习的一个根本缺陷在于其内在依赖积分-放电型点神经元——这类神经元无论当前情境下信息是否相关，均最大化信号传输效率。这导致不必要的神经放电和冲突信息的前馈传递，从而增加学习难度并降低处理能效。本文通过模拟情境敏感的新皮层神经元功能来规避上述局限：该类神经元整合多维来源输入作为情境线索，分别放大与抑制相关及非相关信息的传递。实验证明，由这类局部处理器构成的深度网络会主动增强活跃神经元间的一致性，从而限制冲突信息向高层级传播，并降低处理海量异质现实数据所需的神经活动。相较于现有深度学习范式，这种双点神经元研究展现出显著更优的效能与效率，有望为深度网络架构的细胞层面基础带来变革性突破。