  The field of neuromorphic computing is rapidly evolving. As both biological
accuracy and practical implementations are explored, existing architectures are
modified and improved for both purposes. The Temporal Neural Network(TNN) style
of architecture is a good basis for approximating biological neurons due to its
use of timed pulses to encode data and a voltage-threshold-like system. Using
the Temporal Neural Network cortical column C3S architecture design as a basis,
this project seeks to augment the network's design. This project takes note of
two ideas and presents their designs with the goal of improving existing
cortical column architecture. One need in this field is for an encoder that
could convert between common digital formats and timed neuronal spikes, as
biologically accurate networks are temporal in nature. To this end, this
project presents an encoder to translate between binary encoded values and
timed spikes to be processed by the neural network. Another need is for the
reduction of wasted processing time to idleness, caused by lengthy Gamma cycle
processing bursts. To this end, this project presents a relaxation of Gamma
cycles to allow for them to end arbitrarily early once the network has
determined an output response. With the goal of contributing to the betterment
of the field of neuromorphic computer architecture, designs for both a
binary-to-spike encoder, as well as a Gamma cycle controller, are presented and
evaluated for optimal design parameters, with overall system gain and
performance.
