Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, Department of Experimental Psychology, Oxford University.
Psychol Rev. 2018 Jul;125(4):545-571. doi: 10.1037/rev0000103. Epub 2018 Jun 4.
We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the "holographic principle." (PsycINFO Database Record
我们提出了一个分层神经网络模型，其中神经元的亚群会发展出固定且有规律重复的尖峰时间链（多同步化），这些尖峰时间链会对随机泊松尖峰序列做出特异性反应，这些尖峰序列代表输入的训练图像。通过包括自上而下和侧向的突触连接，以及在每个前后神经元对之间引入多个突触接触，并使不同的突触接触具有不同的轴突延迟，从而提高了性能。因此，尖峰时间依赖性可塑性允许模型在神经元之间选择最有效的轴突传输延迟。此外，通过有视觉引导的学习，代表低水平和高水平视觉特征之间绑定关系的神经元出现了。这开始为解决视觉神经科学中经典的特征绑定问题提供了一种方法，并导致了一个关于如何将关于每个空间尺度的视觉特征的信息通过连续的神经元层向上投射的新假设。我们将这种信息的假设向上投射命名为“全息原理”。
