Dejan Pecevski

TL;DR: In this paper, a review of different aspects of the simulation of spiking neural networks is presented, with the aim of identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking networks.

TL;DR: PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools.

TL;DR: The resulting learning theory predicts that even difficult credit-assignment problems can be solved in a self-organizing manner through reward-modulated STDP, and provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems.

TL;DR: Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization and can be scaled up to neural emulations of probabilistic inference in fairly large graphical models.

TL;DR: This paper investigates how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PC SIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds.