| Summary: | For goal-directed learning in spiking neural networks, target spike templates are usually required.Optimal performance is achieved by minimising the error between the desired and output spike timings.However, in some dynamic environments, a set of learning targets with precise encoding is not always available.For this study, we associate a pair of spatio-temporal events with a target response using a reinforcement learning approach.The learning is implemented in a recurrent spiking neural network using reward-modulated spike-time-dependent plasticity.The learning protocol is simple and inspired by a behavioural experiment from a neuropsychology study.For a goal-directed application, learning does not require a target spike template.In this study, convergence is measured by synchronicity of activities in associated neuronal groups.As a result of learning, a network is able to associate a pair of events with a temporal delay in a dynamic setting. The results demonstrate that the algorithm can also learn temporal sequence detection.Learning has also been tested in face-voice association using real biometric data.The loose dependency between the model's anatomical properties and functionalities could offer a wide range of applications, especially in complex learning environments.
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