Reliable counting of weakly labeled concepts by a single spiking neuron model

Making an informed, correct and quick decision can be life-saving. It's crucial for animals during an escape behaviour or for autonomous cars during driving. The decision can be complex and may involve an assessment of the amount of threats present and the nature of each threat. Thus, we should expect early sensory processing to supply classification information fast and accurately, even before relying the information to higher brain areas or more complex system components downstream. Today, advanced convolution artificial neural networks can successfully solve such tasks and are commonly used to build complex decision making systems. However, in order to achieve excellent performance on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and training samples, only trainable on cloud-computing clusters. A single spiking neuron has been shown to be able to solve many of these required tasks for homogeneous Poisson input statistics, a commonly used model for spiking activity in the neocortex; when modeled as leaky integrate and fire with gradient decent learning algorithm it was shown to posses a wide variety of complex computational capabilities. Here we improve its learning algorithm. We also account for more natural stimulus generated inputs that deviate from this homogeneous Poisson spiking. The improved gradient-based local learning rule allows for significantly better and stable generalization and more efficient performance. We finally apply our model to a problem of multiple instance learning with counting where labels are only available for collections of concepts. In this counting MNIST task the neuron exploits the improved algorithm and succeeds while out performing the previously introduced single neuron learning algorithm as well as conventional ConvNet architecture under similar conditions.