Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition

2023-07-15 · Vijay Shankaran Vivekanand, Rajkumar Kubendran

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.

📄 PDF Abstract BibTeX arXiv:2307.07869

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Tasks

Anomaly Detection Keyword SpottingTime Series

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