Pattern recognition with spiking neural networks: a simple training method

As computers are getting more pervasive, software becomes transferable to different types of hardware and, at the extreme, being bio-compatible. Recent efforts in Artificial Intelligence propose that software can be trained and taught instead of “hard-coded” sequences. This paper addresses the learnability of software in the context of platforms integrating biological components. A method for training Spiking Neural Networks (SNNs) for pattern recognition is proposed, based on spike timing dependent plasticity (STDP) of connections. STDP corresponds to the way connections between neurons change according to the spiking activity in the network, and we use STDP to stimulate outputs of the network shortly after feeding it with a pattern as input, thus creating specific pathways in the network. The computational model used to test this method through simulations is developed to fit the behaviour of biological neural networks, showing the potential for training neural cells into biological processors.