Parkinson's Treatment Emulation Using Asynchronous Cellular Neural Networks

The human brain can be considered as one of the most complex systems found in nature and its underlying physical and biological mechanisms have attracted an increasing research interest especially in reference to modeling of pathogenic neurological conditions. In particular, one of the most well-known paradigms of such pathological neurological conditions that occasionally appear to stress hard the mental and physical condition of elderly people is the Parkinson's disease. Despite the recent progress of the aforementioned disease modeling as well as its tentative treatment by usage of software techniques like the Deep Brain Stimulation (DBS) approach, there is a even growing demand for real-time emulations of such neuromorphic conditions to handle more efficiently such situations. This work proposes a hardware accelerator employing Asynchronous Cellular Neural Networks (ACNNs) in Field Programmable Gate Arrays (FPGAs) to boost further Parkinson's disease studies. Our results suggest that the proposed hardware design provides sufficient neuromorphic plausibility by integrating Synapse and Axon models. As a proof-of-concept, an implementation of the proposed model on Virtex-7 XC7VX330T FPGA has successfully simulated up to 50 Izhikevich neurons in real time with low-power consumption aiming to Parkinson's Disease Treatment emulation by employing the DBS approach.