Robotic implementation of classical and operant conditioning within a single SNN architecture
This work presents the implementation of operant conditioning (OC) and classical conditioning (CC) with a single spiking neural network (SNN) architecture, thus suggesting that the two types of leaning may relate to the same cognitive process. Both are achieved by using a modified version of spike timing dependent plasticity (STDP) where the connection weight between a cue neuron and an action neuron depends on the temporal relation between their spikes and those of a reward neuron. This reward driven STDP (RD STDP) was implemented with simple computational resources to form an electronic robot's brain, using an adaptation of the synapto dendritic kernel adapting neuron (SKAN) model. Then, a robot driven by the new neuronal architecture was tested in a maze with changing features, successfully exhibiting CC and OC. These results and the simple computational resources used make the proposed architecture promising for very large scale time dependent parallel data analysis, with high capacity of adaptation in a dynamic environment. Moreover, it proposes a theoretic framework to model learning by conditioning.
2016 IEEE 15th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC)