Framsticks

This work presents implementation of the Scalar Timing Model (STM) in the neural networks environment. STM is rather popular and commonly used model in the perception of time intervals in humans and animals fields of study. Currently many experiments are conducted in order to verify and research STM parameters and attributes. One of the goal of the implementation was to check whether theoretical model will cope with constraints of artificial neural networks. During implementation process it turned out, that scheme of the model should be revised (by adding extra components) in order to maintain it's functional adequacy. Another case was to check how does manipulations of certain parameters will influence collected representation of the real time within model. In this preliminary research we focus on the pacemaker module. Conclusion of this research is that appropriate choice of distribution form of impulses generated by pacemaker make it simulation of the model more congruent with the experimentally collected data then with formal assumptions of STM.