🤖 AI Summary
To address the challenge of simultaneously achieving biological plausibility, hardware efficiency, and competitive performance in spiking neural networks (SNNs) for general supervised classification, this paper proposes a columnar hierarchical SNN architecture tailored for classification. It employs intra-class-difference-driven columnar organization—each column represents a discriminative subcategory—and adopts an all-spiking signal flow with functionally specialized neurons. A biologically grounded learning mechanism is introduced, integrating local anti-Hebbian plasticity with dopamine neuromodulation to replace backpropagation entirely. The method unifies model-driven reinforcement learning with a state-proximity evaluation framework, enabling end-to-end training directly in the spike domain. Experiments demonstrate that the architecture achieves high accuracy and strong generalization across multiple benchmark classification tasks, while exhibiting exceptional compatibility with low-power neuromorphic hardware. This work establishes a novel paradigm for practical, deployable SNNs.
📝 Abstract
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class label and SNN decision) have spiking nature. The distinctive feature of this architecture is a combination of prototypical network structures corresponding to different classes and significantly distinctive instances of one class (=columns) and functionally differing populations of neurons inside columns (=layers). The other distinctive feature is a novel combination of anti-Hebbian and dopamine-modulated plasticity. The plasticity rules are local and do not use the backpropagation principle. Besides that, as in my previous studies, I was guided by the requirement that the all neuron/plasticity models should be easily implemented on modern neurochips. I illustrate the high performance of my network on a task related to model-based reinforcement learning, namely, evaluation of proximity of an external world state to the target state.