RM-SORN: a reward-modulated self-organizing recurrent neural network
By Witali Aswolinskiy, Gordon Pipa. 2015
13 citations recorded by the source corpus.
Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.
Subfields: 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 Cognitive Neuroscience
In Electrical and Electronic Engineering
Citations in the corpus
- Lifelong learning of human actions with deep neural network self-organization (2017, background): “Additional principles that play a role in lifelong learning such as the influence of reward-driven self-organization (Aswolinskiy & Pipa, 2015) and attentional functions (Ivanov, Liu, Clerkin, Schulz, Friston, Newcorn, & Fan, 2012) in the development of topological maps will be subject to future…”