Weronika Wojtak

The firefly algorithm is a powerful optimization method inspired by the flashing behavior of fireflies. Expand

We propose and test a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. Expand

We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which supports the existence of localized activity patterns or “bumps” with a continuum of amplitudes. Expand

We show that the robot is able to efficiently adapt its motor timing and to store this information in memory using the temporal mismatch between the neural processing of the sensory feedback about executed actions. Expand

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Expand

A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations to endow a robot with these cognitive functions. Expand