Continuous-time spiking neural networks: paradigm and case studies

In the last decades many neuron models have been proposed in order to emulate the spiking behavior of the cortical neurons, from the simplest Integrateand- Fire to the most bio-realistic Hodgkin-Huxley model. The choice of which model have to be used depends on the trade-off between bio-plausibility and computational cost, that may be related to the specific purpose. The modeling of a continuous-time spiking neural network is the main purpose of this thesis. The “continuous-time” term refers to the fact that a spike can occur at any given time, thus in order to do exact computations without loss of information an exact ad hoc event-driven strategy for simulations has been implemented. In particular, the latter is suitable for the simplified neuron model here used. Despite its simplicity, the model shows some important bio-plausible behaviors, such as subthreshold decay, spike latency, refractoriness, etc. Moreover, some bio-inspired synaptic plasticity rules have been implemented (e.g., STDP). With the aim of taking into account non-local interconnections among populations of neurons, gammadistributed synaptic delays are also introduced. These characteristics make possible to investigate various scenarios in which the dynamics showed by the network can be more bio-realistic. Further, some case studies are illustrated: jitter phenomenon and “path multimodality” in feedforward networks, and dynamical activity groups for CNN-like topologies. Finally, future directions of this work are briefly discussed.