neural network emulation: a cost-effective fpga synaptic coupling approach

Efficiently realizing the synaptic coupling of neural networks holds profound significance in the diagnosis and management of neurological disorders, such as epilepsy. in the context of epilepsy research, neuronal synchronization takes center stage, as it directly influences the onset and propagation of seizures. this paper introduces a novel approach to synaptically couple two fitz-hugh nagumo (fhn) neuron approximations, offering a robust and practical design solution. by accurately replicating various behaviors exhibited by interconnected neurons, this proposed multiplierless model has the potential to unveil critical insights into the aberrant neural circuits responsible for epilepsy. through physical synthesis and fpga implementation, our study demonstrates that the presented synaptic coupling approximation maintains a substantially low hardware footprint while delivering superior performance. this technological advancement not only aids in the understanding of epilepsy s neural underpinnings but also holds promise as a diagnostic tool. by examining the synaptic connectivity patterns in epileptic individuals, healthcare professionals may be able to identify specific circuit irregularities, paving the way for more precise and tailored treatments for this neurological condition.