Effects of Temporal Integration on Computational Performance of Spiking Neural Network

In spiking neural networks (SNN), information is considered to be encoded mainly in the temporal patterns of their firing activity. Temporal integration of information plays a crucial role in a variety of cognitive processes, such as sensory discrimination, decision-making, or interval timing. However, it is rarely considered in traditional computational SNN models. In this paper, we investigate the influence of temporal integration on the computational performance of liquid state machine (LSM) from two aspects: the synaptic decay constant and time delay from presynaptic neurons to the output neurons. LSM is a biologically spiking neural network model for real-time computing on time-varying inputs, where the high dimensionality of dynamical spikes is transformed into smoothly changing states through synaptic integration into the readout neuron. Our experimental results show that increasing the decay constant of synapses from SNN to the output neuron can remarkably improve the computational performance due to the enhancement of temporal integration. Moreover, transmission delays have an even larger impact on the richness of dynamical states, which in turn significantly increase the computational accuracy of SNN. These results may have important implications for the modeling of spiking neural networks with excellent computational performance.