## 1 Introduction  
Recent technological advances in terms of hardware and software over the last few decades have unleashed the computational capabilities of modern processors, so as to tackle stringent problems with unparalleled efficiency. These approaches combine the state-of-the-art in Complementary Metal Oxide Semiconductor (CMOS) technology with optimized von Neuman architectures. Despite their unprecedented high-speed performance, modern processors still stagger in addressing a vast area of computational problems within the discipline of machine learning, such as machine vision, natural language processing and decision making [1]. The main two limiting factors of conventional processing architectures are the memory bottleneck and the large energy consumption originating from the physical separation of the data-storing and data-processing units [1]. In addition, well-studied impediments, such as the fan-in/bandwidth tradeoff, also hinder performance enhancement [2; 3]. To overcome these restrictions, brain-inspired architectures such as spiking neural networks have risen as a promising alternative computational paradigm. Although the brain remains vastly unexplored, it is widely accepted that its neurosynaptic layout, where memory and processing units are collocated [4; 5] can alleviate the aforementioned restrictions. By mimicking the brain's framework and function, spiking neural networks encode incoming analogue data to a sparse train of spikes, where information resides in the temporal domain. These features result in a significant reduction of energy consumption while at the same time rendering the computational scheme resilient to noise [6; 7; 8]. A crucial aspect of realizing such biomimicking neural networks is the choice of a technological platform that can efficiently address the above-mentioned issues. Photonic platforms, in particular, have drawn a lot of attention due to the similarity of the dynamics observed in their optical components to real biological neurons [9]. Moreover, inherent advantages such as the high firing rate, low propagation losses, high wall-plug efficiency and more importantly time/wavelength and space multiplexing capabilities, render photonics as one of the best platforms to emulate neural activity [10; 11; 12]. A multitude of photonic spiking neurons have therefore been studied both theoretically and experimentally, such as: two-section gain-absorber lasers [13], microring and disk lasers [14; 15; 16], single section quantum dot lasers [17; 18], nanocavities based on 2D photonic crystals [19; 20], optically injected lasers [21; 22], lasers subjected to optical feedback [23; 24] and vertical cavity surface emitting lasers (VCSELs) [9; 25; 26; 27; 28; 29; 30; 31; 32; 33]. VCSEL neurons exhibit especially interesting aspects such as low power consumption, small footprint and 2D-array integration capabilities [25]. On the other hand, despite their efficiency, previous works mainly focused on the observed dynamics of a single node, and evaluation of a full-scale photonic spiking neural network (PSNN); the targeting of \"real\" applications is still limited [26; 27; 28]. Previous works targeting VCSEL networks consist of a two-layer network based on supervised learning aiming at digit classification [29] and a pattern detection network using a single time-multiplexed neuron without a sophisticated training technique [30; 31]. Alternatively, similar VCSEL-networks have been tested in tasks such as mimicking basic mammalian vision functionalities [32] and in emulating logical gates [33]. An interesting PSNR aims at letter classification task, but involves phase changing materials and avoids using excitable optical neurons [34]. A critical aspect is that all the aforementioned approaches are limited to swallow neural architectures with one [30; 31] or two layers [30; 33]. On the other hand, multilayer networks, have been proven to be capable of feature extraction, which is a significant aspect of typical convolutional neural networks [35].