A Spiking Binary Neuron -- Detector of Causal Links

TL;DR

This paper introduces a simple, hardware-efficient spiking binary neuron that detects causal links in neural signals, improving reinforcement learning capabilities in spiking neural networks with biological plausibility and competitive accuracy.

Contribution

The paper presents a novel spiking binary neuron with specialized synaptic plasticity rules for causal detection, emphasizing hardware-friendliness and biological realism.

Findings

Achieves satisfactory accuracy compared to machine learning methods

Handles single spikes and spike bursts effectively

Enables complex environment operation with multi-neuron structures

Abstract

Causal relationship recognition is a fundamental operation in neural networks aimed at learning behavior, action planning, and inferring external world dynamics. This operation is particularly crucial for reinforcement learning (RL). In the context of spiking neural networks (SNNs), events are represented as spikes emitted by network neurons or input nodes. Detecting causal relationships within these events is essential for effective RL implementation. This research paper presents a novel approach to realize causal relationship recognition using a simple spiking binary neuron. The proposed method leverages specially designed synaptic plasticity rules, which are both straightforward and efficient. Notably, our approach accounts for the temporal aspects of detected causal links and accommodates the representation of spiking signals as single spikes or tight spike sequences (bursts), as observed in biological brains. Furthermore, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. Being compared with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks, our neuron demonstrates satisfactory accuracy despite its simplicity. In conclusion, we introduce a multi-neuron structure capable of operating in more complex environments with enhanced accuracy, making it a promising candidate for the advancement of RL applications in SNNs.