Title
Learning by Stimulation Avoidance as a Primary Principle of Spiking Neural Networks Dynamics
Paper 37 Details
Authors
Presentation Schedule
- Talk Time: 11:20
- Date: Thursday 23 July
- Session: Learning and Evolutionary Software Systems RCH/250 Thursday 23 July 11:20
Full Paper Download
Download via this paper's page on the MIT Press ECAL 2015 Proceedings website.
Abstract
Practical implementation of the concept of reward has deep implications on what artificial-life based systems can learn and how they learn it. How can a system distinguish between useful behavior and harmful behavior? In this paper we implement reward/punishment as the removal/application of a stimulation to a recurrent spiking neural network with spiketiming dependent plasticity. This implementation embodies the concept of reward at the level of the neuron, making learning mechanisms ubiquitous to the network. We show that this low-level learning scales up to the network level: the network learns arbitrary spatio-temporal firing patterns purely by interacting with the environment, from a random initial state where virtually no knowledge is available. This approach yields fast, noise-robust results.
