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In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by locally connected layers in SNNs using spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via competitive inhibitory interactions to learn features from different locations of the input space. These Locally-Connected SNNs (LC-SNNs) manifest key topological features of the spatialarXiv:1904.06269v1 fatcat:k7lc7nzchrezlfwbzrhzjaxel4