Draculab: A Python Simulator for Firing Rate Neural Networks With Delayed Adaptive Connections

• Main Authors: Sergio Verduzco-Flores, Erik De Schutter
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-04-01
• Series: Frontiers in Neuroinformatics
• Subjects: neural simulator; firing rate activity; Python; transmission delay; adaptive synapses
• Online Access: https://www.frontiersin.org/article/10.3389/fninf.2019.00018/full

Draculab is a neural simulator with a particular use scenario: firing rate units with delayed connections, using custom-made unit and synapse models, possibly controlling simulated physical systems. Draculab also has a particular design philosophy. It aims to blur the line between users and developers. Three factors help to achieve this: a simple design using Python's data structures, extensive use of standard libraries, and profusely commented source code. This paper is an introduction to Draculab's architecture and philosophy. After presenting some example networks it explains basic algorithms and data structures that constitute the essence of this approach. The relation with other simulators is discussed, as well as the reasons why connection delays and interaction with simulated physical systems are emphasized.