Hierarchy Of Time-Surfaces is a neuromorphic algorithm used to extract features from patterns of events [1]. This is possible thanks to a type of representation called time-surface or time vector, where events are interpolated by exponential decay kernels and collected to represent relative time differences between the activation of units in the network. Time surfaces are one of the most common representations in the neuromorphic field since they allow to interface event data with traditional machine learning and computer vision algorithms [2, 3]. In HOTS, time surfaces are clustered together using algorithms like k-means to extract common activity patterns, and layers of units are built by considering each centroid as a neuron that can emit a new event when an input time surface is assigned to it. For this reason, HOTS shares many points in common with bag-of-words or bag-of-features algorithms[4]. For instance, HOTS requires an external classifier on histograms of features to classify information. Similarly to bag-of-words algorithms, HOTS classifiers are histograms that accumulate features over a given temporal window to produce an input vector to traditional machine learning algorithms like Support Vector Machines and Multi-Layer Perceptrons[5, 6, 1, 7]. This approach limits compatibility with neuromorphic hardware and can nullify latency and energy efficiency advantages that are found in neuromorphic systems. Compared to Spiking Neural Networks (SNNs) trained with backpropagation through time, HOTS lags in accuracy [8, 9, 10, 1].