Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Abstract

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

“…We collected the key parameters of the learning algorithms and assessed the complexity of the algorithms. We hope that this work will be useful mainly for researchers considering the use of neurocomputation in the analysis of data from various types of sensors [ 36 , 37 , 38 ]. In chapter two, we will briefly discuss the most common neuron models, synapse models, and input encoding types.…”

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI

“…The spiking neural network (SNN), known as the third generation of the neural network, has been introduced into many application fields including electrocardiogram heartbeat classification [ 3 ], object recognition [ 4 ], waveform analysis [ 5 ], odor data classification [ 6 ], and image classification [ 7 ]. SNN has the potential to effectively process spatial-temporal information.…”