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- W4384644016 abstract "Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data. The algorithm leverages a combination of reward-modulatory behavior, Hebbian and anti-Hebbian based learning methods to identify patterns in dynamic datasets with short intervals of training. The algorithm begins with a preprocessing step, where the input data is rationalized and translated to a feature-rich yet sparse spike time series data. Next, a linear feed forward spiking neural network processes this data to identify a trained pattern. Finally, the next layer performs a weighted check to ensure the correct pattern has been detected.To evaluate the performance of the proposed algorithm, it was trained on a complex dataset containing spoken digits with spike information and its output compared to state-of-the-art." @default.
- W4384644016 created "2023-07-19" @default.
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- W4384644016 date "2023-07-15" @default.
- W4384644016 modified "2023-10-18" @default.
- W4384644016 title "Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition" @default.
- W4384644016 doi "https://doi.org/10.48550/arxiv.2307.07869" @default.
- W4384644016 hasPublicationYear "2023" @default.
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