Cristini, AlessandroSalerno, MarioSusi, GianlucaBassis, SimoneEsposito, AnnaMorabito, Francesco Carlo2025-01-282025-01-282015Cristini A, Salerno M, Susi G (2015). A Continuous-Time Spiking Neural Network Paradigm. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham.978331918163997833191816462190-30182190-302610.1007/978-3-319-18164-6_6https://hdl.handle.net/20.500.14352/116519Se deposita la versión corregida y aceptadaIn this work, a novel continuous-time spiking neural network paradigm is presented. Indeed, because of a neuron can fire at any given time, this kind of approach is necessary. For the purpose of developing a simulation tool having such a property, an ad-hoc event-driven method is implemented. A simplified neuron model is introduced with characteristics similar to the classic Leaky Integrate-and-Fire model, but including the spike latency effect. The latency takes into account that the firing of a given neuron is not instantaneous, but occurs after a continuous-time delay. Both excitatory and inhibitory neurons are considered, and simple synaptic plasticity rules are modeled. Nevetheless the chance to customize the network topology, an example with Cellular Neural Network (CNN)-like connections is presented, and some interesting global effects emerging from the simulations are reported.engbook parthttps://link.springer.com/chapter/10.1007/978-3-319-18164-6_6restricted access004.81Neuron ModelSpike LatencySpiking Neural NetworkSynaptic PlasticityContinuous-Time ParadigmEvent-Driven SimulationMatemáticas (Matemáticas)Bioinformática22 Física2404 Biomatemáticas