From Tutorial on Neural Systems Modeling, Chapter 7: Simultaneous perturbative reinforcement learning is effective not only in two-layered but also in three-layered neural networks. Input-hidden, hidden-output, and bias weights can all be perturbed simultaneously. We will use perturbative reinforcement learning (the directed drift algorithm) in this sec­tion to show how two different input signals are distributed over the hidden units in a three-layered, feedforward neural network. Specifically, we will use perturbative reinforcement learning to reproduce the results on the formation of a non-uniform distributed representation that we obtained in Chapter 6 using back-propagation. This simulation was a simplified version of a neural network model of distributed parallel processing in the vestibulo-oculomotor system (see Chapter 6).

NanoBio Node, University of Illinois Champaign-Urbana

Researchers should cite this work as follows:

• Tutorial on Neural Systems Modeling, Copyright 2010 Sinauer Associates Inc. Author: Thomas J. Anastasio

• AbderRahman N Sobh, Jessica S Johnson, NanoBio Node (2014), "[Illinois]: Perturbative Reinforcement Learning to Develop Distributed Representations," https://nanohub.org/resources/pertdistrep. (DOI: 10.4231/D3FF3M12N).

  BibTex | EndNote

1. Illinois
2. MCB 493
3. nano/bio
4. NanoBio Node at Illinois
5. Neural Systems Modeling