Benchmarking Spiking Neural Network Learning Methods with Varying
  Locality
Jiaqi Lin Sen Lu Malyaban Bal Abhronil Sengupta
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics,
have shown to achieve performance comparable to Artificial Neural Networks
(ANNs) in several machine learning tasks. Information is processed as spikes
within SNNs in an event-based mechanism that significantly reduces energy
consumption. However, training SNNs is challenging due to the
non-differentiable nature of the spiking mechanism. Traditional approaches,
such as Backpropagation Through Time (BPTT), have shown effectiveness but comes
with additional computational and memory costs and are biologically
implausible. In contrast, recent works propose alternative learning methods
with varying degrees of locality, demonstrating success in classification
tasks. In this work, we show that these methods share similarities during the
training process, while they present a trade-off between biological
plausibility and performance. Further, this research examines the implicitly
recurrent nature of SNNs and investigates the influence of addition of explicit
recurrence to SNNs. We experimentally prove that the addition of explicit
recurrent weights enhances the robustness of SNNs. We also investigate the
performance of local learning methods under gradient and non-gradient based
adversarial attacks.