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• W2789667046 abstract "Abstract 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." @default.
• W2789667046 created "2018-03-29" @default.
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• W2789667046 date "2018-07-01" @default.
• W2789667046 modified "2023-10-17" @default.
• W2789667046 title "Spatio-temporal event association using reward-modulated spike-time-dependent plasticity" @default.
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• W2789667046 doi "https://doi.org/10.1016/j.ins.2018.03.043" @default.
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