  Spiking Neural Networks (SNNs), recognized for their biological plausibility
and energy efficiency, employ sparse and asynchronous spikes for communication.
However, the training of SNNs encounters difficulties coming from
non-differentiable activation functions and the movement of spike-based
inter-layer data. Spike-Timing Dependent Plasticity (STDP), inspired by
neurobiology, plays a crucial role in SNN's learning, but its still lacks the
conscious part of the brain used for learning. Considering the issue, this
research work proposes a Consciousness Driven STDP (CD-STDP), an improved
solution addressing inherent limitations observed in conventional STDP models.
CD-STDP, designed to infuse the conscious part as coefficients of long-term
potentiation (LTP) and long-term depression (LTD), exhibit a dynamic nature.
The model connects LTP and LTD coefficients to current and past state of
synaptic activities, respectively, enhancing consciousness and adaptability.
This consciousness empowers the model to effectively learn while understanding
the input patterns. The conscious coefficient adjustment in response to current
and past synaptic activity extends the model's conscious and other cognitive
capabilities, offering a refined and efficient approach for real-world
applications. Evaluations on MNIST, FashionMNIST and CALTECH datasets showcase
$CD$-STDP's remarkable accuracy of 98.6%, 85.61% and 99.0%, respectively, in a
single hidden layer SNN. In addition, analysis of conscious elements and
consciousness of the proposed model on SNN is performed.
