Beliebiger Eintrag,
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
(2019)cite arxiv:1904.06269Comment: 22 pages, 7 figures, and 4 tables.
Zusammenfassung
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 spatial interaction of
biological neurons. We explore biologically inspired n-gram classification
approach allowing parallel processing over various patches of the the image
space. We report the classification accuracy of simple two-layer LC-SNNs on two
image datasets, which match the state-of-art performance and are the first
results to date. LC-SNNs have the advantage of fast convergence to a dataset
representation, and they require fewer learnable parameters than other SNN
approaches with unsupervised learning. Robustness tests demonstrate that
LC-SNNs exhibit graceful degradation of performance despite the random deletion
of large amounts of synapses and neurons.
