Cartoon Network: A tool for open-ended exploration of neural circuits.
Robert Calin‐Jageman
- 发表年份
- 2017
- 引用次数
- 4
摘要
Cartoon Network is an open-source simulator for neural circuits. It was designed to provide a microworld for the playful exploration of neural networks (similar to the niche Logo/Scratch fills for computer programming). The simulator makes it easy to construct and experiment with closed-loop circuits, enabling students to explore how small sets of neurons can generate complex patterns of activity (oscillations, reverberation). Cartoon Network can be connected to the Finch robot from BirdBrain Technologies, a cheap USB robotics platform. This enables students to design a nervous system for a Finch, creating sensory neurons which read Finch inputs (touch, light, and temperature), motor neurons which control Finch outputs (wheels, lights, and sound), and interneurons to produce intrinsic activity and/or link together inputs and outputs in ways that can produce complex and surprising behaviors. Students use Cartoon Network by taking on structured challenges (For example, can you make the Finch follow a light? Can it turn and run when it bumps a wall?) that require deep engagement with important principles of neural circuit operation (e.g., lateral inhibition, parallel processing, positive- and negative-feedback). On successfully completing a challenge, students can systematically explore the neural properties that help control the behavior, compare their work to other successful designs, and/or reflect on how the circuit they designed might be modified via evolution to produce different behaviors. Cartoon Network has proven an engaging and effective activity for undergraduates and is accessible for students as young as late elementary school. Download it for free at: https://github.com/rcalinjageman/cartoon_network.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002