  In this study, we propose the first hardware implementation of a
context-based recurrent spiking neural network (RSNN) emphasizing on
integrating dual information streams within the neocortical pyramidal neurons
specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models,
essential element in RSNN. We present a quantized version of the CLIF neuron
(qCLIF), developed through a hardware-software codesign approach utilizing the
sparse activity of RSNN. Implemented in a 45nm technology node, the qCLIF is
compact (900um^2) and achieves a high accuracy of 90% despite 8 bit
quantization on DVS gesture classification dataset. Our analysis spans a
network configuration from 10 to 200 qCLIF neurons, supporting up to 82k
synapses within a 1.86 mm^2 footprint, demonstrating scalability and efficiency
