Feasibility Analysis of Introducing Negative Number to Improve Spiking Neural Network

The aim of this paper is to investigate the feasibility of adding negative numbers to the traditional Leaky Integrate-and-Fire (LIF) for Spiking Neural Network (SNN). Currently the mainstream Spiking neuron is LIF. It is well utilized in some spiking models, but the outputs of these models can only be 0 and 1, implying that they somehow reduce the information transfer represented by negative numbers. This implies that the current SNN research on introducing negative numbers is inadequate. In this paper, by borrowing the idea of Hyperbolic tangent function and the phenomenon of biological neurons transmitting inhibitory and excitatory transmitters, the Ternary Leaky Integrate-and-Fire model (TLIF) which can output 0, -1 and 1 is proposed by improving the LIF model. And by designing a simple recognition model and comparing the recognition accuracy of LIF and TLIF models under the same dataset training, it is found that TLIF can have a better recognition effect in dealing with some datasets, but its performance is worse than LIF when facing more complex data. As the result, the introduction of negative numbers is feasible for SNNs, but further research is needed for the specific improvement methods. improvement methods and neural network design need further research.