A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

By Michael John O'Brien, Narayan Srinivasa. 2013

19 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Florian Röhrbein
2. J. Krichmar
3. Andrea Soltoggio

In Cognitive Neuroscience

1. Andrea Soltoggio
2. Florian Röhrbein
3. J. Krichmar
4. Kenneth O. Stanley
5. Sebastian Risi
6. Jeff Clune
7. Jean-Baptiste Mouret
8. Paul Tonelli

In Electrical and Electronic Engineering

1. J. Krichmar
2. Andrea Soltoggio
3. Jeff Clune
4. Kenneth O. Stanley
5. Roby Velez

Citations in the corpus

• Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, methodology): “Previous models (Izhikevich, 2007; O’Brien and Srinivasan, 2013; Soltoggio and Steil, 2013) show a separation the of weight values between reward-inducing synapses (high values) and other synapses (low values).”
• Value and reward based learning in neurorobots (2013, background): “(2011); O’Brien and Srinivasa (2013) will be explored in the future.”