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

• URL: http://arxiv.org/abs/2103.08143v1
• Date: Mon, 15 Mar 2021 05:22:14 GMT
• Title: Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks
• Authors: Oleg Nikitin and Olga Lukyanova and Alex Kunin
• Abstract summary: We show how cellular dynamics help neurons to filter out the intense signals to help neurons keep a stable firing rate. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant information. Such processing is often associated with Bayesian inference. Yet most common models of neural cells, including biologically plausible, such as Hodgkin-Huxley or Izhikevich do not possess predictive dynamics on the level of a single cell. 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. In the present article, we will introduce new mechanics of interconnection between neuron firing rate homeostasis and weight change by means of STDP growth bounded by abstract protein reserve, controlled by the intracellular optimization algorithm. We will show, how these cellular dynamics help neurons to filter out the intense signals to help neurons keep a stable firing rate. We will 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.

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