The principle of weight divergence facilitation for unsupervised pattern recognition in spiking neural networks
This work addresses the challenge of enhancing unsupervised learning in neuromorphic systems, but it appears incremental as it builds on existing STDP rules with a novel control mechanism.
The paper tackles the problem of unsupervised pattern recognition in spiking neural networks by proposing a method that uses STDP synaptic plasticity to optimize weight modification based on noise-to-signal ratios, resulting in improved signal-to-noise ratio in experiments with various input regimes.
Parallels between the signal processing tasks and biological neurons lead to an understanding of the principles of self-organized optimization of input signal recognition. In the present paper, we discuss such similarities among biological and technical systems. We propose adding the well-known STDP synaptic plasticity rule to direct the weight modification towards the state associated with the maximal difference between background noise and correlated signals. We use the principle of physically constrained weight growth as a basis for such weights' modification control. It is proposed that the existence and production of bio-chemical 'substances' needed for plasticity development restrict a biological synaptic straight modification. In this paper, the information about the noise-to-signal ratio controls such a substances' production and storage and drives the neuron's synaptic pressures towards the state with the best signal-to-noise ratio. We consider several experiments with different input signal regimes to understand the functioning of the proposed approach.