Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

TL;DR

This paper proposes a biologically inspired mechanism using constrained plasticity reserves to regulate neuron firing rates and weights, enhancing noise filtering and stability in spiking neural networks.

Contribution

It introduces a novel plasticity regulation method based on protein reserves that links firing rate homeostasis with synaptic weight adaptation.

Findings

Neurons can filter out intense noise signals effectively.

The proposed mechanism maintains stable firing rates without impairing input recognition.

The approach improves robustness of neural computations against noisy inputs.

Abstract

Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant information. However, most common neural cell models, including biologically plausible, such as Hodgkin-Huxley or Izhikevich, do not possess predictive dynamics on a single-cell level. Moreover, the modern rules of synaptic plasticity or interconnections weights adaptation also do not provide grounding for the ability of neurons to adapt to the ever-changing input signal intensity. While natural neuron synaptic growth is precisely controlled and restricted by protein supply and recycling, weight correction rules such as widely used STDP are efficiently unlimited in change rate and scale. The present article introduces new mechanics of interconnection between neuron firing rate homeostasis and weight change through STDP growth bounded by abstract protein reserve, controlled by the intracellular optimization algorithm. We show how these cellular dynamics help neurons filter out the intense noise signals to help neurons keep a stable firing rate. We also examine that such filtering does not affect the ability of neurons to recognize the correlated inputs in unsupervised mode. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.