Date of Award


Publication Type


Degree Name



Electrical and Computer Engineering


Majid Ahmadi


Arash Ahmadi



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Spiking Neural Network which is known as the third generation of artificial neural networks can imitate the same biological patterns of human brain. Neuromorphic Computing is a multidisciplinary research topic which employs digital and analog platforms to implement bio-inspired systems and is able to operate parallelly with low-power consumption. This research study presents the digital design for several bio-inspired systems such as biophysical spiking neuron, calcium signaling astrocyte, and a neuron-astrocyte interaction models using various approximation methodologies. Astrocytes have an essential impact within the neural network and their role is to maintain support for neuron, control the ion hemostasis and synaptic strength. Therefore, two different hardware designs based on piece-wise linear and CORDIC algorithms are presented for two various Ca2+-based signalling models. The modified structure is implemented on FPGA and implementation results can prove that the same spiking patterns of the original models are observed for the modified models. These proposed structures can be considered for neuromorphic implementation of the large-scale neuro-inspired systems. A digital architecture is presented for conductance based adaptive exponential integrate and fire (CAdEx) neuron model according to COordinate Rotation DIgital Computer (CORDIC) paradigm and the synthesis result shows that the modified structure is able to reproduce the same spiking activities of the original biological model. A hardware comparison has been performed between the modified design and the previously published digital designs for other neuro-inspired system. A neuromorphic design by realizing the bidirectional commination between neurons and astrocytes within the neuroglial network and its interaction model is presented which can be considered for implementation of rehabilitation neuro-prosthetic devices. Thus, a modified neuron-astrocyte interaction (tripartite synapse) model based on leaky and integrate fire neuron and the astrocyte-synapse models using an area-efficient hardware approach called stochastic computing paradigm. The proposed model is synthesized physically on field-programmable gate array as a proof of concept. The implementation results of the presented model can mimic the bidirectional communication in biological minimal network of pre-postsynaptic and Ca2+-based model for astrocyte with considerably lower hardware cost. The influence of astrocytes on neural network behavioral has been investigated by providing a proper feedback mechanism and considering the role of gap junction coupling and the various coefficients on desynchronizing the impaired synchronization of the coupled neurons. The hardware implementation of the biologically plausible neuron and glial cell models may assist to reveal accurately to the diagnostic and treatment approaches of the neurological disorders. Thus, this paper presents a low-cost hardware approach according to coordinate rotation digital computer (CORDIC) method for a deep brain stimulator to investigate the therapeutic conditions for neuropathological states of Alzheimer’s disease (AD). As a proof of concept, a FPGA implementation is performed on the modified biologically plausible components of the proposed stimulator. The results of physical implementation and the numerical analysis illustrate that, the proposed bio-inspired brain stimulator design can assist the transition of the aberrant ICW patterns into the normal biochemical circumstances. As a result, the proposed deep brain stimulation strategy based on the modified models can be a good candidate for the further development of bio-robotics systems and rehabilitation therapy of AD at early stages.