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. Our results show that, in
the case of audio-visual processing, nets composed of context-sensitive local
processors can use video information as a context that guides audio signal
processing towards the currently relevant information far more effectively and
efficiently than current forms of DL.

通过模仿具有环境感知性的新皮层神经元的能力，使用视频信息作为上下文来引导音频信号处理，既能提高深度学习在音频 - 视觉处理方面的处理效能，又能提高处理效率和能源利用效率。

上下文敏感的新皮层神经元转换神经信息处理的有效性和效率

Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing

Neocortical neurons have thousands of excitatory synapses. It is a mystery
how neurons integrate the input from so many synapses and what kind of
large-scale network behavior this enables. It has been previously proposed that
non-linear properties of dendrites enable neurons to recognize multiple
patterns. In this paper we extend this idea by showing that a neuron with
several thousand synapses arranged along active dendrites can learn to
accurately and robustly recognize hundreds of unique patterns of cellular
activity, even in the presence of large amounts of noise and pattern variation.
We then propose a neuron model where some of the patterns recognized by a
neuron lead to action potentials and define the classic receptive field of the
neuron, whereas the majority of the patterns recognized by a neuron act as
predictions by slightly depolarizing the neuron without immediately generating
an action potential. We then present a network model based on neurons with
these properties and show that the network learns a robust model of time-based
sequences. Given the similarity of excitatory neurons throughout the neocortex
and the importance of sequence memory in inference and behavior, we propose
that this form of sequence memory is a universal property of neocortical
tissue. We further propose that cellular layers in the neocortex implement
variations of the same sequence memory algorithm to achieve different aspects
of inference and behavior. The neuron and network models we introduce are
robust over a wide range of parameters as long as the network uses a sparse
distributed code of cellular activations. The sequence capacity of the network
scales linearly with the number of synapses on each neuron. Thus neurons need
thousands of synapses to learn the many temporal patterns in sensory stimuli
and motor sequences.

本研究提出一个神经元和网络模型，其中神经元能够学习和记忆序列模式，网络能够实现时间序列的鲁棒学习，并在大量噪音和多样化数据的情况下，精准地识别数百个唯一的细胞活动模式。