Pair-Associate Learning in Spiking Neutral Networks.

We propose associative learning models that integrate spike-time dependent plasticity (STDP) and firing rate in two semi-supervised paradigms, Pavlovian and reinforcement learning. Through the Pavlovian approach, the learning rule associates paired stimuli (stimulus-stimulus) known as the predictor-choice pair. Synaptic plasticity is dependent on the timing and the rate of pre- and post synaptic spikes within a time window. The contribution of our learning model can be attributed to the implementation of the proposed learning rules using integration of STDP and firing rate in spatio-temporal neural networks, with Izhikevich’s spiking neurons. There is no such model yet found in the literature. The model has been tested in recognition of real visual images. As a result of learning, synchronisation of activity among inter- and intra-subpopulation neurons demonstrates association between two stimulus groups. As an improvement to the stimulus-stimulus (S-S) association model, we extend the algorithm for stimulus-stimulus-response (S-S-R) association using a reinforcement approach with reward-modulated STDP. In the later model, firing rate in response groups determines a reward signal that modulates synaptic changes derived from STDP processes. The S-S-R model has been successfully tested in a visual recognition task with real images and simulation of the colour word Stroop effect. The learning algorithm is able to perform pair-associate learning as well as to recognise the sequence of the presented stimuli. Unlike other existing gradient-based learning models, the S-S-R model implements temporal sequence learning in more natural way through reward-based learning whose protocol follows a behavioural experiment from a psychology study. The key novelty of our S-S-R model can be ascribed to its lateral inhibition mechanism through a minimal anatomical constraint that enables learning in high competitive environments (e.g. temporal logic AND and XOR problems). The S-S model models for example the retrospective and prospective activity in the brain, whilst the S-S-R model exhibits reward acquisition behaviour in human learning. Furthermore, we have proven than, a goal directed learning can be implemented via a generic neural network with rich realistic dynamics based on neurophysiological data. Hence the loose dependency between the model’s anatomical properties and functionalities could offer a wide range of applications especially in complex learning environments