  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.
