At present, a major initiative in the research community is investigating new ways of processing data that capture the efficiency of the human brain in hardware and software. This has resulted in increased interest and development of bio-inspired computing approaches in software and hardware. One such bio-inspired approach is Cellular Simultaneous Recurrent Networks (CSRNs). CSRNs have been demonstrated to be very useful in solving state transition type problems, such as maze traversals. Although powerful in image processing capabilities, CSRNs have high computational demands with increasing input problem size. In this work, we revisit the maze traversal problem to gain an understanding of the general processing of CSRNs. We use a 2.67 GHz Intel Xeon X5550 processor coupled with an NVIDIA Tesla C2050 general purpose graphical processing unit (GPGPU) to create several novel accelerated CSRN implementations as a means of overcoming the high computational cost. Additionally, we explore the use of decoupled extended Kalman filters in the CSRN training phase and find a significant reduction in runtime with negligible change in accuracy. We find in our results that we can achieve average speedups of 21.73 and 3.55 times for the training and testing phases respectively when compared to optimized C implementations. The main bottleneck in training performance was a matrix inversion computation. Therefore, we utilize several methods to reduce the effects of the matrix inversion computation.