This paper presents a biologically-inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the mo...
Chris Christodoulou, Guido Bugmann, Trevor G. Clar...
The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A lear...
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
Abstract. We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks [1] as models of contextual deterministic discrete dynamical systems. We show that ch...
We seek a framework that addresses localization, detection and recognition of man-made objects in natural-scene images in a unified manner. We propose to model artificial structur...