For energy-efficient computation in specialized neuromorphic hardware, we present the Spiking Neural Coding Network, an instantiation of a family of artificial neural models strongly motivated by the theory of predictive coding. The model, in essence, works by operating in a never-ending process of "guess-and-check", where neurons predict the activity values of one another and then immediately adjust their own activities to make better future predictions. The interactive, iterative nature of our neural system fits well into the continuous time formulation of data sensory stream prediction and, as we show, the model's structure yields a simple, local synaptic update rule, which could be used to complement or replace online spike-timing dependent plasticity. In this article, we experiment with an instantiation of our model that consists of leaky integrate-and-fire units. However, the general framework within which our model is situated can naturally incorporate more complex, formal neurons such as the Hodgkin-Huxley model. Our experimental results in pattern recognition demonstrate the potential of the proposed model when binary spike trains are the primary paradigm for inter-neuron communication. Notably, our model is competitive in terms of classification performance, can conduct online semi-supervised learning, naturally experiences less forgetting when learning from a sequence of tasks, and is more computationally economical and biologically-plausible than popular artificial neural networks.
翻译:对于专门神经形态硬件的节能计算,我们展示了Spiking神经编码网络,这是一个由预测编码理论所强烈推动的人工神经模型组成的组合的即时反应。该模型本质上通过一个永无止尽的“猜测和检查”过程运作,神经元预测彼此的活动值,然后立即调整其自身活动,以作出更好的未来预测。我们神经系统的交互性、迭接性特性非常适合数据感官流预测的持续时间配置,正如我们所显示的那样,该模型的结构产生一个简单的、本地合成的更新规则,可以用来补充或取代在线快速刺激依赖的可塑性。在这个文章中,我们通过对模型的即时即时操作,神经元预测包含彼此的活动值,然后立即调整其自身的活动,以便做出更好的未来预测。我们的神经系统的互动性能与数据感官流预测的连续设计非常适合。正如我们所显示的那样,当我们提议的模型的双向峰值更新规则成为在线快速同步更新规则时,可以用来补充或取代在线快速快速快速快速快速快速快速移动的网络运作模式,我们进行实验的模型的模型学习过程,在自然、不甚甚甚易地、不易地、不易地进行生物内核化的计算学的计算中学习。