A Spiking Bidirectional Associative Memory Neural Network

Abstract

Spiking neural networks (SNNs) are a more biologically realistic model of the brain than traditional analog neural networks and therefore should be better for modelling certain functions of the human brain. This thesis uses the concept of deriving an SNN from an accepted non-spiking neural network via analysis and modifications of the transmission function. We investigate this process to determine if and how the modifications can be made to minimize loss of information during the transition from non-spiking to spiking while retaining positive features and functionality of the non-spiking network. By comparing combinations of spiking neuron models and networks against each other, we determined that replacing the transmission function with a neural model that is similar to it allows for the easiest method to create a spiking neural network that works comparatively well. This similarity between transmission function and neuron model allows for easier parameter selection which is a key component in getting a functioning SNN. The parameters all play different roles, but for the most part, parameters that speed up spiking, such as large resistance values or small rheobases generally help the accuracy of the network. But the network is still incomplete for a spiking neural network since this conversion is often only performed after learning has been completed in analog form. The neuron model and subsequent network developed here are the initial steps in creating a bidirectional SNN that handles hetero-associative and auto-associative recall and can be switched easily between spiking and non-spiking with minimal to no loss of data. By tying everything to the transmission function, the non-spiking learning rule, which in our case uses the transmission function, and the neural model of the SNN, we are able to create a functioning SNN. Without this similarity, we find that creating SNN are much more complicated and require much more work in parameter optimization to achieve a functioning SNN.