Spike timing dependent plasticity based enhanced self-learning for efficient pattern recognition in spiking neural networks

Spike Timing Dependent Plasticity (STDP), wherein synaptic weights are modified based on the temporal correlation between a pair of pre- and post-synaptic (post-neuronal) spikes, is widely used to implement unsupervised learning in Spiking Neural Networks (SNNs). In general, STDP-based learning models disregard the information embedded in post-neuronal spiking frequency. We observe that updating the synaptic weights at the instants of every post-neuronal spike while ignoring the spiking frequency could potentially cause them to learn overlapping representations of multiple input patterns sharing common features. We present STDP-based enhanced plasticity mechanisms that account for the spiking frequency to achieve efficient synaptic learning. First, we utilize low-pass filtered neuronal membrane potential to obtain an estimate of the spiking frequency. We perform STDP-driven weight updates in the event of a post-spike if the filtered potential exceeds a definite threshold. This ensures that plasticity is effected on the dominantly firing neuron that indicates a strong bias in learning the input pattern. Synaptic updates are restrained in the case of sporadic neuronal spiking activity, which implies a weak correlation with the input pattern. This enhances the quality of features encoded by the synapses, resulting in an improvement of 5.8% in the classification accuracy of an SNN of 100 neurons trained for digit recognition. Our simulations further show that the enhanced scheme provides a reduction of 2 χ in the number of weight updates, which leads to improved energy efficiency in event-driven SNN implementations. Second, we explore a neuronal spike-count based enhanced plasticity mechanism. The synapses are modified at the instant of a post-spike if the neuron had fired a certain number of spikes since the preceding update instant. This scheme performs delayed updates at suitable neuronal spiking instants to learn improved synaptic representations. Using this technique, the classification accuracy increased by 4% with 5.2× reduction in the number of weight updates.