A Power-Efficient Neuromorphic Digital Implementation of Neural–Glial Interactions

Abstract

Throughout the last decades, neuromorphic circuits have incited the interest of scientists, as they are potentially a powerful tool for the treatment of neurological diseases. To this end, it is essential to consider the biological principles of the CNS and develop the appropriate area- and power-efficient circuits. Motivated by studies that outline the indispensable role of astrocytes in the dynamic regulation of synaptic transmission and their active contribution to neural information processing in the CNS, in this work we propose a digital implementation of neuron–astrocyte bidirectional interactions. In order to describe the neuronal dynamics and the astrocytes’ calcium dynamics, a modified version of the original Izhikevich neuron model was combined with a linear approximation of the Postnov functional neural–glial interaction model. For the implementation of the neural–glial computation core, only three pipeline stages and a 10.10 fixed point representation were utilized. Regarding the results obtained from the FPGA implementation and the comparisons to other works, the proposed neural–glial circuit reported significant savings in area requirements (from 22.53% up to 164.20%) along with considerable savings in total power consumption of 28.07% without sacrificing output computation accuracy. Finally, an RMSE analysis was conducted, confirming that this particular implementation produces more accurate results compared to previous studies.

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

“…[335][336][337][338] Experts from a variety of fields, including neuroscience, computer science, electrical engineering, and materials science, must work together to co-design the complete computing stack in neuromorphic computers. 339 This cooperation is necessary to make sure that design decisions for algorithms, software, and hardware are coordinated and mutually beneficial. It is difficult to co-design the entire computing stack in neuromorphic computers, but doing so could lead to significant improvements in computing power, particularly for tasks that benefit from neuromorphic processing, like pattern recognition, sensor data analysis, and real-time processing.…”