Autonomous Learning Paradigm for Spiking Neural Networks

Compared to biological systems, existing learning systems lack the ability to learn autonomously, especially in changing and dynamic environments. This paper addresses the issue of autonomous learning by developing a self-learning spiking neural network (SNN) and demonstrating its autonomous learning capability using a simple robot controller application. Our proposed learning rule exploits an inherit property of the existing Spike-Timing-Dependent Plasticity (STDP) rule in that if the instantaneous presynaptic frequency decreases, then for a conventional Hebbian window the STDP rule potentiates. Conversely if the instantaneous frequency increases the STDP rule depresses: the opposite is true for anti-Hebbian window. This paper will also show that obstacle avoidance is achievable using a conventional Hebbian learning window while object tracking can be learned using an anti-Hebbian learning window. Hence the proposed learning paradigm is novel in that it does not require external supervisions for either these tasks. The proposed learning paradigm also uses a previously explored astrocyte neuron interaction where a periodic Slow Inward Current (SIC) from an astrocyte can potentiate a postsynaptic neuron for a period of time: this time window can be used to strengthen/weaken synaptic pathways. An obstacle avoidance task is used for the performance analysis and results show that the SNN based robot controller has autonomous learning capabilities under the dynamic conditions.